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Words are the most powerful tools to express our thoughts, opinions, intentions, desires, or preferences. However, they do not have the same meaning in all instances. Instead, the meaning conveyed is mainly shaped by the context. This complexity of human languages constitutes a challenge for AI methods that work with natural languages, such as sentiment analysis. 

Consider the following example:

Figure 1. Consumer feedback on a product

The consumer states in his review that he is content with the product, and his words can be classified as positive (e.g., “love,” “amazing,” and “long battery life”). However, in the fifth sentence, he says that his wife does not have similar thoughts. Instead, her sentiment regarding the product is negative (e.g., “too heavy”). So, how would the algorithm classify this review? As positive, negative, or neutral?

Here are the top five challenges of conducting sentiment analysis and how to solve them:

1. Context-dependent errors Sarcasm

People tend to use sarcasm as a way of expressing their negative sentiment, but the words used can be positive (e.g., “I am so glad that the product arrived in one piece!”). In such cases, sentiment analysis tools can classify the feedback as positive, which in reality is negative.

Solution: Determine the boundaries of sarcasm in the training dataset. For instance, researchers used a multi-head self-attention-based neural network architecture to identify terms that include sarcasm. It highlights the parts that have a sarcastic tone, then connects these parts to each other to obtain an overall score.

Polarity

Although the emotional tone in some sentences can be very apparent and robust (e.g., “It was a terrible experience.”), the others are not easily classified as positive, negative, or neutral (e.g., “The service quality is not mentionable.”). So, the polarity of the statement cannot always be easily inferred by the algorithms.

Solution: Give polarity scores to the words in the training dataset so that the algorithm can classify the difference between statements such as “very good” and “slightly good.”

Polysemy

When words have more than one meaning (e.g., the head of the sales team vs. wearing an earbud hurts the head), then it becomes more challenging for the algorithm to differentiate what the intended meaning is. Thus, as the word is not evaluated in its context, the results of the analysis can be inaccurate.

Solution: Incorporate domain knowledge during text annotation and model training phases. It can help your sentiment analysis algorithms to differentiate between words that have different meanings in different contexts.

For more in-depth knowledge on sentiment analysis, feel free to download our comprehensive whitepaper:

2. Negation Detection 

Just because a sentence contains negation (e.g., no, not, -non, -less, -dis), it does not mean that the overall sentiment of the statement is negative. Current negation detection methods are not sufficient to classify the sentiment correctly. For instance, “It was not unpleasant” is a statement with negation and can be classified by the algorithm as negative, but it conveys a positive meaning. 

Solution: Train your algorithm with large datasets, including all possible negation words. A combination of term-counting methods that regard contextual valence shifters and machine learning methods is found to be effective in identifying negation signals more accurately.

3. Multilingual Data

Although English is the common language used worldwide, as companies grow, they engage with customers globally. This results in customers using different languages while providing feedback. However, the sentiment analysis tools are primarily trained to categorize the words in one language, and some sentiments may get lost in translation. This causes a significant problem, especially while conducting sentiment analysis on non-English reviews or feedback.

Solution: Design systems that can learn from multilingual content and can make predictions regardless of the language. For instance, you can use a code-switching approach that includes parallel encoders at a word and implements models such as deep neural networks. You can also check our article on multilingual sentiment analysis for a comprehensive account.

4. Emojis

Figure 2. The valence and arousal rates for the most used emojis

Emojis have become a part of daily life and are more effective in expressing one’s sentiment compared to words. However, as the sentiment analysis tools depend on written texts, emojis cannot be classified accurately and thus are removed from many analyses. In turn, one ends up with a noncomprehensive analysis.

Solution: Determining the emoji tags and implementing them into your sentiment analysis algorithm can improve the accuracy of your analysis. 

5. Potential Biases in Model Training

Although AI algorithms are powerful tools to make accurate predictions, they are trained by humans. This means that they inevitably reflect human biases in the training dataset in their results. For instance, if the algorithm is trained to label the sentence “I am a sensitive person” as negative and label the sentence “I can be very ambitionist” as positive, the results can be biased towards some people with emotional tendencies and may distinguish overly ambitious people.

Solution: Minimize bias in AI systems by conducting debiasing methods. For instance, you can detect the words in your dataset that might involve human bias and develop a dictionary for these words. This way, you can tag them and then compare the overall sentiment in the text with and without these tagged words.

To learn more about sentiment analysis, read our other articles:

If you think your company can benefit from sentiment analysis, check our data-driven list of sentiment analysis services.

Do not hesitate to contact us if you have any further questions:

Begüm Yılmaz

Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.

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Twitter Sentiment Analysis Using Python

A Twitter sentiment analysis determines negative, positive, or neutral emotions within the text of a tweet using NLP and ML models. Sentiment analysis or opinion mining refers to identifying as well as classifying the sentiments that are expressed in the text source. Tweets are often useful in generating a vast amount of sentiment data upon analysis. These data are useful in understanding the opinion of people on social media for a variety of topics.

This article was published as a part of the Data Science Blogathon.

What is Twitter Sentiment Analysis?

Twitter sentiment analysis analyzes the sentiment or emotion of tweets. It uses natural language processing and machine learning algorithms to classify tweets automatically as positive, negative, or neutral based on their content. It can be done for individual tweets or a larger dataset related to a particular topic or event.

Why is Twitter Sentiment Analysis Important?

Understanding Customer Feedback: By analyzing the sentiment of customer feedback, companies can identify areas where they need to improve their products or services.

Political Analysis: Sentiment analysis can help political campaigns understand public opinion and tailor their messaging accordingly.

Crisis Management: In the event of a crisis, sentiment analysis can help organizations monitor social media and news outlets for negative sentiment and respond appropriately.

How to Do Twitter Sentiment Analysis?

In this article, we aim to analyze Twitter sentiment analysis using machine learning algorithms, the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning pipeline involving the use of three classifiers (Logistic Regression, Bernoulli Naive Bayes, and SVM)along with using Term Frequency- Inverse Document Frequency (TF-IDF). The performance of these classifiers is then evaluated using accuracy and F1 Scores.

For data preprocessing, we will be using Natural Language Processing’s (NLP) NLTK library.

Twitter Sentiment Analysis: Problem Statement

In this project, we try to implement an NLP Twitter sentiment analysis model that helps to overcome the challenges of sentiment classification of tweets. We will be classifying the tweets into positive or negative sentiments. The necessary details regarding the dataset involving the Twitter sentiment analysis project are:

The dataset provided is the Sentiment140 Dataset which consists of 1,600,000 tweets that have been extracted using the Twitter API. The various columns present in this Twitter data are:

target: the polarity of the tweet (positive or negative)

ids: Unique id of the tweet

date: the date of the tweet

flag: It refers to the query. If no such query exists, then it is NO QUERY.

user: It refers to the name of the user that tweeted

text: It refers to the text of the tweet

Twitter Sentiment Analysis: Project Pipeline

The various steps involved in the Machine Learning Pipeline are:

Import Necessary Dependencies

Read and Load the Dataset

Exploratory Data Analysis

Data Visualization of Target Variables

Data Preprocessing

Splitting our data into Train and Test sets.

Transforming Dataset using TF-IDF Vectorizer

Function for Model Evaluation

Model Building

Model Evaluation

Let’s get started,

Step-1: Import the Necessary Dependencies # utilities import re import numpy as np import pandas as pd # plotting import seaborn as sns from wordcloud import WordCloud import matplotlib.pyplot as plt # nltk from chúng tôi import WordNetLemmatizer # sklearn from chúng tôi import LinearSVC from sklearn.naive_bayes import BernoulliNB from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import confusion_matrix, classification_report Step-2: Read and Load the Dataset # Importing the dataset DATASET_COLUMNS=['target','ids','date','flag','user','text'] DATASET_ENCODING = "ISO-8859-1" df = pd.read_csv('Project_Data.csv', encoding=DATASET_ENCODING, names=DATASET_COLUMNS) df.sample(5)

Output:

Step-3: Exploratory Data Analysis

3.1: Five top records of data

df.head()

Output:

3.2: Columns/features in data

df.columns

Output:

Index(['target', 'ids', 'date', 'flag', 'user', 'text'], dtype='object')

3.3: Length of the dataset

print('length of data is', len(df))

Output:

length of data is 1048576

3.4: Shape of data

df. shape

Output:

(1048576, 6)

3.5: Data information

df.info()

Output:

3.6: Datatypes of all columns

df.dtypes

Output:

target int64 ids int64 date object flag object user object text object dtype: object

3.7: Checking for null values

np.sum(df.isnull().any(axis=1))

Output:

0

3.8: Rows and columns in the dataset

print('Count of columns in the data is: ', len(df.columns)) print('Count of rows in the data is: ', len(df))

Output:

Count of columns in the data is: 6 Count of rows in the data is: 1048576

3.9: Check unique target values

df['target'].unique()

Output:

array([0, 4], dtype=int64)

3.10: Check the number of target values

df['target'].nunique()

Output:

2 Step-4: Data Visualization of Target Variables # Plotting the distribution for dataset. ax = df.groupby('target').count().plot(kind='bar', title='Distribution of data',legend=False) ax.set_xticklabels(['Negative','Positive'], rotation=0) # Storing data in lists. text, sentiment = list(df['text']), list(df['target'])

Output:

import seaborn as sns sns.countplot(x='target', data=df)

Output:

Step-5: Data Preprocessing

In the above-given problem statement, before training the model, we performed various pre-processing steps on the dataset that mainly dealt with removing stopwords, removing special characters like emojis, hashtags, etc. The text document is then converted into lowercase for better generalization.

Subsequently, the punctuations were cleaned and removed, thereby reducing the unnecessary noise from the dataset. After that, we also removed the repeating characters from the words along with removing the URLs as they do not have any significant importance.

At last, we then performed Stemming(reducing the words to their derived stems) and Lemmatization(reducing the derived words to their root form, known as lemma) for better results.

5.1: Selecting the text and Target column for our further analysis

data=df[['text','target']]

5.2: Replacing the values to ease understanding. (Assigning 1 to Positive sentiment 4)

data['target'] = data['target'].replace(4,1)

5.3: Printing unique values of target variables

data['target'].unique()

Output:

array([0, 1], dtype=int64)

5.4: Separating positive and negative tweets

data_pos = data[data['target'] == 1] data_neg = data[data['target'] == 0]

5.5: Taking one-fourth of the data so we can run it on our machine easily

data_pos = data_pos.iloc[:int(20000)] data_neg = data_neg.iloc[:int(20000)]

5.6: Combining positive and negative tweets

dataset = pd.concat([data_pos, data_neg])

5.7: Making statement text in lowercase

dataset['text']=dataset['text'].str.lower() dataset['text'].tail()

Output:

5.8: Defining set containing all stopwords in English.

stopwordlist = ['a', 'about', 'above', 'after', 'again', 'ain', 'all', 'am', 'an', 'and','any','are', 'as', 'at', 'be', 'because', 'been', 'before', 'being', 'below', 'between','both', 'by', 'can', 'd', 'did', 'do', 'does', 'doing', 'down', 'during', 'each','few', 'for', 'from', 'further', 'had', 'has', 'have', 'having', 'he', 'her', 'here', 'hers', 'herself', 'him', 'himself', 'his', 'how', 'i', 'if', 'in', 'into','is', 'it', 'its', 'itself', 'just', 'll', 'm', 'ma', 'me', 'more', 'most','my', 'myself', 'now', 'o', 'of', 'on', 'once', 'only', 'or', 'other', 'our', 'ours','ourselves', 'out', 'own', 're','s', 'same', 'she', "shes", 'should', "shouldve",'so', 'some', 'such', 't', 'than', 'that', "thatll", 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'there', 'these', 'they', 'this', 'those', 'through', 'to', 'too','under', 'until', 'up', 've', 'very', 'was', 'we', 'were', 'what', 'when', 'where','which','while', 'who', 'whom', 'why', 'will', 'with', 'won', 'y', 'you', "youd","youll", "youre", "youve", 'your', 'yours', 'yourself', 'yourselves']

5.9: Cleaning and removing the above stop words list from the tweet text

STOPWORDS = set(stopwordlist) def cleaning_stopwords(text): return " ".join([word for word in str(text).split() if word not in STOPWORDS]) dataset['text'] = dataset['text'].apply(lambda text: cleaning_stopwords(text)) dataset['text'].head()

Output:

5.10: Cleaning and removing punctuations

import string english_punctuations = string.punctuation punctuations_list = english_punctuations def cleaning_punctuations(text): translator = str.maketrans('', '', punctuations_list) return text.translate(translator) dataset['text']= dataset['text'].apply(lambda x: cleaning_punctuations(x)) dataset['text'].tail()

Output:

5.11: Cleaning and removing repeating characters

def cleaning_repeating_char(text): return re.sub(r'(.)1+', r'1', text) dataset['text'] = dataset['text'].apply(lambda x: cleaning_repeating_char(x)) dataset['text'].tail()

Output:

5.12: Cleaning and removing URLs

def cleaning_URLs(data): dataset['text'] = dataset['text'].apply(lambda x: cleaning_URLs(x)) dataset['text'].tail()

Output:

5.13: Cleaning and removing numeric numbers

def cleaning_numbers(data): return re.sub('[0-9]+', '', data) dataset['text'] = dataset['text'].apply(lambda x: cleaning_numbers(x)) dataset['text'].tail()

Output:

5.14: Getting tokenization of tweet text

from nltk.tokenize import RegexpTokenizer tokenizer = RegexpTokenizer(r'w+') dataset['text'] = dataset['text'].apply(tokenizer.tokenize) dataset['text'].head()

Output:

5.15: Applying stemming

import nltk st = nltk.PorterStemmer() def stemming_on_text(data): text = [st.stem(word) for word in data] return data dataset['text']= dataset['text'].apply(lambda x: stemming_on_text(x)) dataset['text'].head()

Output:

5.16: Applying lemmatizer

lm = nltk.WordNetLemmatizer() def lemmatizer_on_text(data): text = [lm.lemmatize(word) for word in data] return data dataset['text'] = dataset['text'].apply(lambda x: lemmatizer_on_text(x)) dataset['text'].head()

Output:

5.17: Separating input feature and label

X=data.text y=data.target

5.18: Plot a cloud of words for negative tweets

data_neg = data['text'][:800000] plt.figure(figsize = (20,20)) wc = WordCloud(max_words = 1000 , width = 1600 , height = 800, collocations=False).generate(" ".join(data_neg)) plt.imshow(wc)

Output:

5.19: Plot a cloud of words for positive tweets

data_pos = data['text'][800000:] wc = WordCloud(max_words = 1000 , width = 1600 , height = 800, collocations=False).generate(" ".join(data_pos)) plt.figure(figsize = (20,20)) plt.imshow(wc)

Output:

Step-6: Splitting Our Data Into Train and Test Subsets # Separating the 95% data for training data and 5% for testing data X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.05, random_state =26105111) Step-7: Transforming the Dataset Using TF-IDF Vectorizer

7.1: Fit the TF-IDF Vectorizer

vectoriser = TfidfVectorizer(ngram_range=(1,2), max_features=500000) vectoriser.fit(X_train) print('No. of feature_words: ', len(vectoriser.get_feature_names()))

Output:

No. of feature_words: 500000

7.2: Transform the data using TF-IDF Vectorizer

X_train = vectoriser.transform(X_train) X_test = vectoriser.transform(X_test) Step-8: Function for Model Evaluation

After training the model, we then apply the evaluation measures to check how the model is performing. Accordingly, we use the following evaluation parameters to check the performance of the models respectively:

Accuracy Score

Confusion Matrix with Plot

ROC-AUC Curve

def model_Evaluate(model): # Predict values for Test dataset y_pred = model.predict(X_test) # Print the evaluation metrics for the dataset. print(classification_report(y_test, y_pred)) # Compute and plot the Confusion matrix cf_matrix = confusion_matrix(y_test, y_pred) categories = ['Negative','Positive'] group_names = ['True Neg','False Pos', 'False Neg','True Pos'] group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)] labels = [f'{v1}n{v2}' for v1, v2 in zip(group_names,group_percentages)] labels = np.asarray(labels).reshape(2,2) sns.heatmap(cf_matrix, annot = labels, cmap = 'Blues',fmt = '', xticklabels = categories, yticklabels = categories) plt.xlabel("Predicted values", fontdict = {'size':14}, labelpad = 10) plt.ylabel("Actual values" , fontdict = {'size':14}, labelpad = 10) plt.title ("Confusion Matrix", fontdict = {'size':18}, pad = 20) Step-9: Model Building

In the problem statement, we have used three different models respectively :

Bernoulli Naive Bayes Classifier

SVM (Support Vector Machine)

Logistic Regression

The idea behind choosing these models is that we want to try all the classifiers on the dataset ranging from simple ones to complex models, and then try to find out the one which gives the best performance among them.

8.1: Model-1

BNBmodel = BernoulliNB() BNBmodel.fit(X_train, y_train) model_Evaluate(BNBmodel) y_pred1 = BNBmodel.predict(X_test)

Output:

8.2: Plot the ROC-AUC Curve for model-1

from sklearn.metrics import roc_curve, auc fpr, tpr, thresholds = roc_curve(y_test, y_pred1) roc_auc = auc(fpr, tpr) plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC CURVE') plt.legend(loc="lower right") plt.show()

Output:

8.3: Model-2:

SVCmodel = LinearSVC() SVCmodel.fit(X_train, y_train) model_Evaluate(SVCmodel) y_pred2 = SVCmodel.predict(X_test)

Output:

8.4: Plot the ROC-AUC Curve for model-2

from sklearn.metrics import roc_curve, auc fpr, tpr, thresholds = roc_curve(y_test, y_pred2) roc_auc = auc(fpr, tpr) plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC CURVE') plt.legend(loc="lower right") plt.show()

Output:

8.5: Model-3

LRmodel = LogisticRegression(C = 2, max_iter = 1000, n_jobs=-1) LRmodel.fit(X_train, y_train) model_Evaluate(LRmodel) y_pred3 = LRmodel.predict(X_test)

Output:

8.6: Plot the ROC-AUC Curve for model-3

from sklearn.metrics import roc_curve, auc fpr, tpr, thresholds = roc_curve(y_test, y_pred3) roc_auc = auc(fpr, tpr) plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC CURVE') plt.legend(loc="lower right") plt.show()

Output:

Step-10: Model Evaluation

Upon evaluating all the models, we can conclude the following details i.e.

Accuracy: As far as the accuracy of the model is concerned, Logistic Regression performs better than SVM, which in turn performs better than Bernoulli Naive Bayes.

AUC Score: All three models have the same ROC-AUC score.

We, therefore, conclude that the Logistic Regression is the best model for the above-given dataset.

In our problem statement, Logistic Regression follows the principle of Occam’s Razor, which defines that for a particular problem statement, if the data has no assumption, then the simplest model works the best. Since our dataset does not have any assumptions and Logistic Regression is a simple model. Therefore, the concept holds true for the above-mentioned dataset.

Conclusion

We hope through this article, you got a basic of how Sentimental Analysis is used to understand public emotions behind people’s tweets. As you’ve read in this article, Twitter Sentimental Analysis helps us preprocess the data (tweets) using different methods and feed it into ML models to give the best accuracy.

Key Takeaways

Twitter Sentimental Analysis is used to identify as well as classify the sentiments that are expressed in the text source.

Logistic Regression, SVM, and Naive Bayes are some of the ML algorithms that can be used for Twitter Sentimental Analysis.

Frequently Asked Questions

Related

Top 5 Challenges Of Enterprise Seo

What makes enterprise SEO unique from a general knowledge in SEO?

The complexities.

A larger company has the technical challenges of a complex site architecture to getting budget for the tools and help needed to be successful.

I have seen many SEO professionals come from consulting or agency environments who have strong technical, content, and general knowledge of SEO.

What they lack, however, is the ability to work with other teams or understand the complexities that enterprise organizations face.

Without the experience of working in-house in such an environment, any SEO professional will struggle to gain credibility or make any sort of impact – resulting in a stagnant outcome with no signs of growth.

Here are the top five challenges any SEO might face when working in an enterprise environment:

Complex technical challenges.

Getting buy-in.

Priority for the business.

Tracking effectiveness.

Budget for tools/help.

If you can navigate through these challenges then support from the organization – all the way from the top down – will become strong, allowing for the overall health of the company’s SEO to flourish.

1. Complex Technical Challenges for SEO

When it comes to technical SEO for enterprise organizations, the level of complexity increases tenfold.

Sites that don’t focus on the technical aspects of SEO will often fall short no matter how excellent content, brand recognition, or link authority is.

When a site has 90% of its pages throwing errors around redirect chains, improper or self canonicals, duplicate titles, JavaScript issues, etc. are less likely to get results than their competitors who have only 20% (or less) of those issues.

New projects that are launched are less likely to gain traction in the search results or may struggle to ever get indexed at all.

Larger organizations consist of multiple engineering teams working on several aspects to a site and often on different platforms.

When entering into an enterprise environment, it’s good to understand that your first few months are going to be about learning:

How a site is structured.

How the engineering teams work.

How SEO can play a part.

From the use of subdomains/subdirectories, pushing chúng tôi files, generating chúng tôi identifying pages that need or have noindex tags, pagination, JavaScript implementation, how canonical tags are generated, pagination, infinite scrolling, Ajax calls, and so much more.

All of this may be common knowledge, however, working in the enterprise environment with a large site managed by multiple stakeholders that have business decisions that could impact SEO adds a level of complexity that is a challenge to navigate.

When dealing with the complexities of technical SEO in an enterprise environment, an SEO must not only be knowledgeable but be willing to listen.

A strong enterprise SEO can look at data, analyze crawl reports and web logs, and know who to talk to in order to understand the history of the work that has been done for SEO.

A good SEO should be able to make authoritative decisions while maintaining humbleness as trial and error tests present the best results.

By focusing 30% of the team’s effort on technical SEO fixes and mitigating additional issues, an enterprise site will have greater success.

2. Getting Buy-in

Ask any SEO and they will tell you how obvious it is why companies should invest time and money into SEO.

With a little effort from engineering, some tweaking by the content teams, and a small investment in SEO experts any company could make money with very little overhead.

Unfortunately, not all organizations see it that way.

Whether it’s a lack of understanding of what SEO is or what all that goes into SEO companies don’t often see it as a worthy investment.

SEO professionals should know that working within an organization doesn’t make it any easier to get work done for SEO.

Calendars could be filled with meetings all day but a discussion might come up in a meeting where stakeholders decide that SEO isn’t important and the SEO wasn’t in the room.

Later they find out that after a project was launched the company could have benefited greatly from considering SEO.

The key to getting buy-in for SEO is for an SEO to get to know as many people as possible in the organization from the top down.

Even when it doesn’t seem like someone will ever need or ever work with SEO, it’s still good to get to know them and their role.

One of those people could be in a meeting the SEO is left out of when a group decides that they don’t need SEO. In which case they would be able to speak up for SEO in their absence.

Additionally, any successes the team can have for SEO are always a good way to get organizational buy-in.

Find some pages or a section of the website that could use some changes to help improve SEO and report on the growth that you have accomplished.

This will show stakeholders and decision-makers that some work for SEO can increase traffic and revenue.

They will have more respect for SEO as well as the team that works on SEO and will want the same for their projects and responsibilities.

Getting buy-in from key stakeholders on what it takes to get work done for SEO is one of the biggest struggles any SEO faces in a larger organization.

From convincing the CEO that SEO can increase the bottom line, all the way down to getting engineering teams to spend the time to make their JavaScript crawlable by Google, SEO pros can spend a lot of time getting buy-in.

3. Priority for the Business

I will, at times, refer to SEO as the red-headed stepchild (a phrase used to describe a person who is neglected, mistreated, or unwanted) of the business.

Many companies know that they can benefit from SEO, but don’t understand enough about it to make it a priority.

Organizations that end up in this hole of lack of support, or understanding for SEO, have a difficult time digging themselves out and therefore reaping any benefit from SEO.

While getting stakeholders to buy-into SEO, it’s also important to push to establish SEO as one of the important priorities for the business as a whole.

By communicating with the business showing small, or even large, wins a team can establish SEO as a priority and align with what the business has planned for.

Aligning with the business isn’t always an easy task, and not all organizations are transparent when it comes to communicating what is a priority to the SEO level.

By focusing on getting to know teams and stakeholders within the organization the SEO team could become part of the conversation when there are talks about priorities for the business.

While getting SEO established as a priority for the business is a struggle, the benefits can be astronomical for both the business and the SEO team.

4. Tracking Effectiveness

Many times I have worked with agencies that report on their wins by showing keywords that have moved up in rankings, or pages they have worked on appearing higher in the search results.

The biggest struggle that SEO will often have within a large organization is reporting. Most enterprise businesses expect SEO to impact revenue.

While ranking changes and increases in traffic are nice, everything boils down to how much the company makes.

Tracking keywords to revenue is virtually impossible.

Calculating can be a complex estimation based on formulas with an understanding of what pages showed up for which keyword searches, with a count of keywords to that page, cut by percentages of Google traffic from organic from how much that page generated in revenue.

It’s a formula that isn’t always doable for each business, especially in times where there are multiple pages that appear in search results for a keyword.

The best way to track SEO’s effectiveness is to understand what is important for the business.

Some businesses are happy with free signups and might have a revenue value associated with them.

Working with data scientists and understanding hurdles associated when reporting for SEO is one of the biggest struggles an SEO faces in a larger organization.

5. Budget for Tools/Help

One of the biggest struggles I have faced in all my years as an SEO and working for many enterprise organizations is getting support and budget to hire help (agencies, consultants, or staff) and the tools needed that can handle large complex systems.

Hiring agencies that have the technical knowledge, experience, and understanding of what it takes to manage SEO for large organizations are few and far in-between, and they don’t come cheap.

Getting budget support for the expert help needed takes a lot of hard work and scrappiness from the SEO (or team) that is in place.

SEO professionals shouldn’t be expected to know everything there is to know about all aspects of SEO.

Some are more versed in content, or some may be more technically savvy.

Some may have a good sense for navigating the red tape of the organization but aren’t strong in content or technical. In that case, hiring a consultant or an agency to help to make the team even stronger would lead the business into a strong SEO presence.

The best way to approach this is for the existing team to not be afraid to speak up and let their coworkers know when they aren’t strong in an aspect of SEO.

Most organizations will respect this and support the hiring of an agency or consultant that specializes in that part that is lacking.

If SEO has shown wins from technical fixes and mitigation then an expert or agency that can help put a content roadmap together with a growth plan would make sense.

The key is to show stakeholders that there can be success with one aspect of SEO and that there is potential for growth from others. In the end, everyone will benefit.

At the enterprise level, well-known tools like Moz, DeepCrawl, and Keylime Toolbox aren’t able to handle the complexities or the massiveness of enterprise sites.

For larger organizations, tools that are capable of handling complex sites, and that have the staff of support that know what SEO professionals at that level face, require big budgets.

Getting budget approval for these tools can often be a struggle, but when the support comes in and the tools are being utilized, SEO greatly benefits in the long term.

The key is to start small with the more widely known tools and focus on a smaller part of a site.

Use the data found to gain some wins while expressing that there are tools that are helping, but there are struggles due to their limited capabilities to manage larger sites.

When SEO brings in several billion in revenue for a business and the team is asking for a tool that is a very small fraction of that, the return on investment makes sense.

Summary

The challenges that enterprise SEO professionals face are definitely unique.

But these aren’t impossible impossible to overcome.

By understanding that enterprise SEO has its own struggles and having the patience and experience to navigate through them, any SEO in a larger organization can have a successful career and the business will benefit in the long run.

Featured Image Credit: Paulo Bobita

Does Google Use Sentiment Analysis To Rank Web Pages?

Many SEOs believe that the sentiment of a web page can influence whether Google ranks a page. If all the pages ranked in the search engine results pages (SERPs) have a positive sentiment, they believe that your page will not be able to rank if it contains negative sentiments.

The evidence and facts are out there to show where Google’s research has been focusing in terms of sentiment analysis.

I asked Bill Slawski (@bill_slawski)  , an expert in Google related patents what he thought about the SEO theory that Google uses sentiment analysis to rank web pages.

“Sentiment is like a flavor, like vanilla or chocolate. It does not reflect the potential information gain that an article might bring.

Information gain can be understood by using NLP processing to extract entities and knowledge about them, and that can lead to a determination of information gain.

Sentiment is a value that doesn’t necessarily reflect how much information an article might bring to a topic.

Positive or negative sentiment is not a reflection of how much knowledge is present and added to a topic.”

Bill affirmed that Google tends to show a range of opinions for review related queries.

“I don’t believe that Google would favor one sentiment over another. That smells of showing potential bias on a topic.

I would expect Google to want some amount of diversity when it comes to sentiment, so if they were considering ranking based upon it, they would not show all negative or positive.”

Bill makes an excellent point about the lack of usefulness if Google search results introduced a sentiment bias.

Some SEOs believe that if all the search results have a positive sentiment, then that’s a reflection of what searchers are looking for. That’s a naive correlation.

There are many known ranking factors such as links that can account for those rankings. There are other factors such as users wanting to see specific sites for specific queries.

Simply isolating one factor and saying, “Aha, all the sites have this so this is why it’s ranking” is naive, it’s cherry picking what you want to see.

For example, the same SEO can look at those search results and see that they all use the same brand of SEO plugin. Does that mean the SEO plugin is the reason those sites rank?

The answer is no.

Similarly, the sentiment expressed in the search results does not necessarily reflect what the searcher is looking for.

This is why I say it is naive to look at one factor such as sentiment and say that’s the reason a site is ranking. Just because you see a correlation does not mean it’s the reason a site is ranking.

Does Google Use Sentiment Analysis for Ranking?

Google’s been largely silent on sentiment analysis since 2023.

In July 2023, someone on Twitter asked:

“…it seems like your search algorithm recognizes and takes into account sentiment. Is there a sentiment search operator?”

Danny Sullivan answered:

“It does not recognize sentiment. So, no operator for that.”

Danny made it clear that Google’s search algorithm does not recognize sentiment.

Earlier that year Danny published an official Google announcement about featured snippets where he mentioned sentiment. But the context of sentiment was that for some queries there may be a diversity of opinions and because of that Google might show two featured snippets, one positive and one negative.

“…people who search for “are reptiles good pets” should get the same featured snippet as “are reptiles bad pets” since they are seeking the same information: how do reptiles rate as pets? However, the featured snippets we serve contradict each other.

A page arguing that reptiles are good pets seems the best match for people who search about them being good. Similarly, a page arguing that reptiles are bad pets seems the best match for people who search about them being bad. We’re exploring solutions to this challenge, including showing multiple responses.”

The point of the above section is that they are exploring showing multiple responses.

Since 2023, Google has stopped showing featured snippets for vague queries like “are reptiles good pets?” and encouraging users to drill down and choose a more specific reptile.

Danny wrote:

Those statements directly contradicts the SEO idea that if the sentiment in the SERPs leans in one direction, that your site needs to lean in the same direction to rank.

Rather, Google is asserting that they want to show diversity in opinions.

Positives and Negatives in Reviews

A Google research paper titled, Structured Models for Fine-to-Coarse Sentiment Analysis (PDF 2007) states that a “question answering system” would require sentiment analysis at a paragraph level.

A system that summarizes reviews would need to understand the positive or negative opinion at the sentence or phrase level.

This is sometimes referred to as opinion mining. The point of this kind of analysis is to understand the opinion.

Here’s how the research paper explains the importance of sentiment analysis:

“The ability to classify sentiment on multiple levels is important since different applications have different needs. For example, a summarization system for product reviews might require polarity classification at the sentence or phrase level; a question answering system would most likely require the sentiment of paragraphs; and a system that determines which articles from an online news source are editorial in nature would require a document level analysis.”

The paper further describes how sentiment analysis is useful:

2004). One interesting work on sentiment analysis is that of Popescu and Etzioni (2005) which attempts to classify the sentiment of phrases with respect to possible product features.”

What stands out about that research is that it is strictly about understanding the sentiment of text.

There is no context for using it to show search results that are biased toward the sentiment in a  user’s search query.

The context is not about ranking text according to the sentiment.

Yet even though the context is not about ranking because of the sentiment, some SEOs will quote this kind of research and then tack on that it’s being used for ranking. And that’s wrong because the context of this and other research papers are consistently about understanding text, well outside of the context of ranking that text.

Sentiment Analysis Encompasses More than Positive and Negative

Another research paper, What’s Great and What’s Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis (PDF 2010) presents a way to understand the sentiment of product reviews.

The scope of the research is finding a better way to deal with ambiguity in the way ideas are expressed.

Examples of these kinds of linguistic negation phrases are:

“Given the poor reputation of the manufacturer, I expected to be disappointed with the device. This was not the case.”

“Do not neglect to order their delicious garlic bread.”

“Why couldn’t they include a decent speaker in this phone?”

The above examples show how this research paper is focused on understanding what humans mean when they structure their speech in a certain way. This is an example of how sentiment analysis is about more than just positive and negative sentiment.

It’s really about the meaning of words, phrases, paragraphs and documents.

The paper begins by stating the usefulness of sentiment analysis in several scenarios, including question answering:

“The automatic detection of the scope of linguistic negation is a problem encountered in wide variety of document understanding tasks, including but not limited to medical data mining, general fact or relation extraction, question answering, and sentiment analysis.”

How would accurately classifying these kinds of sentences help a search engine in question answering?

A search engine cannot accurately answer a question without understanding the web pages it wants to rank.

It’s not about using that data as ranking factors. It’s about using that data to understand the pages so that they then can then be ranked according to ranking criteria.

One way of looking at sentiment analysis is to think of it as obtaining candidate web pages for ranking. A search engine cannot select a candidate if it cannot understand the web page.

Once a search engine can understand a web page, it can then apply the ranking criteria on the pages that are likely to answer the question.

This is especially important for search queries that are ambiguous because of things like linguistic negation, as described in the research paper above.

If sentiment analysis is used by Google, a web page isn’t ranked because of the sentiment analysis. Sentiment analysis helps a web page be understood so that it can be ranked.

Google can’t rank what it can’t understand. Google can’t answer a question that it can’t understand.

More Sentiment Analysis Research

SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis (PDF 2014)

This research paper studies how to better understand what users mean when they leave online reviews on websites, forums, microblogs and so on.

This is how it describes the problem being solved:

“…most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis.”

Speech Sentiment Analysis via End-To-End ASR Features (PDF 2023)

ASR means Automatic Speech Recognition. This research paper is about understanding speech, and doing things like giving more weight to non-speech inflections like laughter and breathing.

The research shares examples of using breathing and laughter as weighted elements to help them understand the sentiment in the context of speech sentiment analysis, but not for ranking purposes.

These are the examples:

4. That would be wonderful, that would be great seriously. “

The paper describes the context of where it is useful:

“Speech sentiment analysis is an important problem for interactive intelligence systems with broad applications in many industries, e.g., customer service, health-care, and education.

The task is to classify a speech utterance into one of a fixed set of categories, such as positive, negative or neutral.”

This research is very new, from 2023 and while not obviously specific to search, it’s indicative of the kind of research Google is doing and how it is far more sophisticated than what the average reductionist SEO sees as a simple ranking factor.

No Sentiment Analysis Bias at Google

Google has consistently stated that they try not to show pages that reflect a searcher’s sentiment intent (are geckos bad pets?)

In fact, Google says the opposite, that it tries to show a diversity of opinions. Google tries not to be led by a sentiment expressed in the search query.

Example of Google Showing Diversity of Opinion

As you can see in the above screenshot, Google does not allow the negative sentiment expressed in the search query to influence it into showing a web page with a negative sentiment.

This directly contradicts the idea that Google shows search results with a specific sentiment bias if that bias exists in the search query.

You can dig around for Google research and patents about sentiment analysis and you will see that the context is about understanding search queries and web pages.

You will not see research that says the sentiment will be used to rank a page according to its bias.

If the pages that Google is ranking all have the same sentiment, do not assume that that is why those pages are there.

It is clear from Google research papers, statements from Google and from Google search results that Google does not allow the sentiment of the user search query to influence the kind of sites that Google will rank.

Top 5 Fintech Growth Ideas In 2023

What is Fintech?

FinTech stands for Financial Technology- sounds easy, right?

FinTech includes a large collection of goods, technology, and business units which are changing the financial services sector.

Or then again on the off chance that you give to some crusade or asset, or check your ledger articulation online-that is likewise FinTech.

Because of FinTech, you can without much of a stretch take a credit or insurance simply by using your telephone.

Global Investments in Fintech:

Buyers are adopting FinTech quick.

3 out of each 4 global shoppers across 27 significant economies utilize a cash move and installments FinTech administration.

India and China are ahead of the pack with the greater part of the shoppers of FinTech. The appropriation rate is 87% for both these countries.

This selection is by using administrations like cash moves, financial planning, borrowing and insurance administrations.

FinTech has made up for a shortcoming for individuals who don’t approach customary banking administrations. It is assessed that around two billion individuals worldwide don’t have financial balances.

Considering this immense interest, the leading business places of the world have moved to FinTech to investigate additional opportunities.

Albeit, in 2023, FinTech organizations saw investments drop by more than 33%, reaching 105.3 billion dollars.

A portion of the world’s greatest organizations like Apple and Google are going enthusiastic about FinTech as well. Apple has concocted Apple Pay and Google with GPay.

Growth of FinTech in India

The high growth pace of FinTech in India can be ascribed to the increase in the prevalence of computerized installments.

This growth began when the Indian government demonetized the 500 and 1000 rupee notes, back in 2024.

This move incited a many individuals and businesses to go credit only. I’m very certain you were one of them.

Demonetization behaved like rocket fuel for India’s FinTech space.

The reception of FinTech has been high to such an extent that in excess of a fifth of all investments in 2023 was into FinTech new companies. This investment remained at 3.2 billion dollars out of the 14.5 billion dollars, which was raised by the startup biological system back in 2023.

In 2023, India got 2.7 billion dollars in FinTech investments.

So much has been happening in the FinTech space; however a lot more should be finished.

FinTech Ideas For The Year 2023

Here are the top 5 FinTech ideas that are having great opportunities to dive deep into:

1. Alternative credit scoring

The FICO score FinTech organizations can trade out and foster frameworks which are options in contrast to the regular rating techniques.

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2. Alternative insurance underwriting

In the event that you have bought an online insurance strategy, you realize that your insurance charge is chosen by your age, and whether you are a smoker or a non-smoker.

In any case, imagine a scenario where you are an activity freak. For what reason would it be a good idea for you to pay a similar premium as your companion of a similar age who is a habitually lazy person? Is it true that he is bound to kick the bucket of diabetes than I am?

FinTech organizations can concoct calculations and frameworks that consider the way of life of an individual to determine his/her extra security premium.

Information points can be gathered from web-based media profiles, rec center enrollments and clinical records. These information points can be added to the customary information points to make it more quantifiable.

The summed up suppositions are obsolete in this day and age.

Carpe Data is one such FinTech organization which depends on intelligent and self learning calculations to get information from web-based media profiles, way of life indicators and clinical records to choose charges and customized terms and conditions.

3. Asset Management

Many mutual fund start-ups are offering zero commission platforms to their users.

But Fintech companies are going a step further. They are offering zero commission platforms for stocks trading as well and gathering data from the users.

This data is being passed on to higher frequency traders in their community, which results in an increase in the price of that particular asset.

Regulations for such practices differ from nation to nation. But this is something on the ascendency for sure, on which the FinTechs can capitalize further.

Mutual fund companies are dependent on investor’s banks for speedy registration of the One-Time-Mandate (OTM) for their monthly SIPs (systematic investment plans). This process is time consuming, especially if it is a public sector bank.

Cancellation of these one-time-mandates is also a cumbersome task. Many asset management companies and banks still rely on physical forms for carrying out this process.

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4. Transaction delivery

Data is the new gold. Many FinTech start-ups are offering free services just to gather information about the users.

For instance, a cost the executives application is allowing its clients to utilize cost the board benefits free of charge. However, simultaneously, it assembles all the information regarding the costs of the client.

This information can be imparted to other FinTech organizations, or partners to make a focused on deals technique.

Clients these days accept focused on and modified items as an extravagance. A ‘positive sentiment’ is connected with something that is modified distinctly for you among so numerous others.

FinTechs need to trigger these generosity suppositions within clients by customizing encounters for them.

5. Peer-to-peer lending

Shared (P2P) lending isn’t new to the finance world. Individuals have been borrowing cash from others for seemingly forever now.

FinTech organizations are coming up with stages facilitating distributed lending. The loan specialists are considered and pre-supported to stay away from income issues.

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Survival of the fittest

So, these are a few of the very best Fintech suggestions for the year 2023 and you may have already figured that FinTech is a lively business. Each day brings a fresh idea on the tableon someone else’s if none!

Top 5 Hotels In Bhopal (2023 List)

District Bhopal

In terms of tourism, there are numerous tourist attractions in and around Bhopal. The city’s architecture is a mix of ancient and modern. Bhopal is situated in central India and is known for its natural beauty. Lakes and hills surround the town. Visitors can explore a variety of tourist attractions and also find a range of hotels in Bhopal to cater to their accommodation needs.

Hotels in Bhopal  #1 Madhuban Resort

This is among the finest Hotels in Bhopal. The Ratapani Wildlife Sanctuary’s lush forest surrounds Madhuban Resort, providing a real ecotourism experience. This resort offers a variety of lodging alternatives, ranging from elegant Swiss Cottages to camping tents, each offering a different panoramic perspective of the surrounding area.

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Address: Sarkanpur, Road, Dongri, Madhya Pradesh 466446

Map: Google Map

Price: Approx INR 4000

Check-Out Time: 10:00 AM

Amenities:

Wifi

Luggage Storage

Room Service

Daily Housekeeping

Facilities:

Dining

Free Parking

24 hours security

How to Reach?

Bhopal train station is about 90 kilometers away and closest to the resort.

Bhopal airport is 95 kilometers from Rethi and is accessible via train from major cities. There are flights from major cities to Bhopal.

Nearby Attractions:

Ginnorgarh Fort (7.7 km)

Vindhyachal Mountain (11.5 km)

Bhada Bhada Waterfall (16.2 km)

Ratapani Jungle Safari (7.9 km)

Delawadi Waterfalls (12.6 km)

#2 Crescent Resort

The Crescent Resort allows you to relax to the fullest because it has practically every type of entertainment available, resulting in a fun and unforgettable visit. Spend some time during your visit to “The City of Lakes” at the Crescent Resort with loved ones.

Address: New, Sehore Bypass Rd, Sehore, Madhya Pradesh 466001

Map: Google Map

Price: Approx INR 4000

Check-in Time: 02:00 PM

Check-out Time: 12:00 PM

Amenities:

Room service

Laundry and dry cleaning

Free Parking

Free wifi

Facilities:

Swimming Pool

Dining

Gym Facility

Party Lawn

Play Area

How to Reach?

The resort is 45 kilometers from Bhopal Railway Station.

Bhopal Airport is 20 kilometers away, while Indore Airport is 180 kilometers away. There are direct flights from major cities to Indore.

Nearby Attractions:

Upper Lake (31 km)

#3 Nature Courtyard

Look only as far as Nature Courtyard if you’re seeking a charming Bhopal guest house. The location is excellent, tranquil, and peaceful, away from the hustle and bustle of the city while still being close to tourist attractions.

Address: court, Nature courtyard, Kachnar, Kerwa Dam Rd, near National Law Institute University, NLIU, Madhya Pradesh 462044

Map: Google Map

Check-In Time: 12:00 PM

Check-Out Time: 11:00 AM

Amenities:

Room service

Free wifi

Free Parking

Facilities:

Dining

How to Reach?

Bhopal train station is approximately 20 kilometers away from the resort.

Bhopal airport is approximately 30 kilometers from the resort.

Nearby Attractions:

EAGLE Point (800 m)

Kerwa Dam (4.7 km)

Kaliasot Picnic Spot (2.1 km)

#4 The Lake Bourbon

The Lake Bourbon is an excellent choice for those looking for an apartment in Kopal, Bhopal. This apartment is one of the most highly regarded Hotels in Bhopal. It is a couple-friendly hotel, so unmarried couples can easily stay here.

Address: Barkhedi kalan, Bhadbhada Road, Kopal, Bhopal, Madhya Pradesh 462044

Map: Google Map

Price: Approx INR 2000

Check-In: 12:00 PM

Check-Out: 11:00 AM

Swimming Pool

Free wifi

Free Parking

Transportation

Facilities:

Bar

Dining

How to Reach?

Bhopal railway station is the closest to the resort, around 13 kilometers away.

Bhopal Airport is about 20 kilometers away from the resort.

Nearby Attractions:

EAGLE Point (1.8 km)

Kerwa Dam (5.8 km)

Kaliasot Picnic Spot (3.2 km)

Bhadbhada hills (3.2 km)

#5 Jehan Numa Retreat

Beautiful green carpets surround Jehan Numa Retreat, which shares its limits with Van Vihar Urban National Park, and also features a drive-through zoo. This magnificent resort offers a boutique escape with a rustic setting within the park’s beautiful flora.

Address: Vanvihar Rd, Van Vihar National Park, Near, Prempura, Bhopal, Madhya Pradesh 462002

Map: Google Map

Price: Approx INR 16000

Check-in Time: 12 PM

Check-out Time: 12 PM

Amenities:

Swimming Pool

Wifi

Room Service

Facilities:

Dining

Spa

Fitness Centre

How to Reach?

The nearest railway station is roughly 11 kilometers from the resort, and it takes approximately 24 minutes to arrive by private or public transportation.

Bhopal Airport is the closest airport to the resort, located 17.5 kilometers away and easily accessible by both public and private transportation.

Nearby Attractions:

Van Vihar National Park (3.3 km)

Sair Sapata Bhopal (800 m)

State Museum of Madhya Pradesh (3.8 km)

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