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The race has been going on since the first silicon computer chips began to appear. Hardware manufacturers have constantly been one-upping each other in a frenzy to cram as many transistors as possible into tinier and tinier spaces. In 2014 Intel celebrated the release of processors featuring transistors about 6,000 times smaller than the diameter of a single strand of hair. This is a far cry, however, from the dream of achieving the manufacture of molecular-level transistors. On 17 June 2024, a group of researchers in the Peking University in Beijing may have proven that this dream may be closer to reality than we think. As the race for smaller hardware continues, we may as well dive into what this may mean for us and what challenges manufacturers may face in trying to make molecule-sized technology a reality.

The Problem with the Word “Molecule”

Whenever we think of a molecule, we think of something extraordinarily small – something so small it can only be observed with highly-specialized equipment. The problem is that, unlike atoms, molecules do not always come in such microscopic dimensions. When someone tells me they have made a transistor that consists of a single molecule, the first question that comes to mind is, “What kind of molecule are we talking about?”

A molecular chain can be enormous. Polymers such as the DNA inside every cell of your body can measure anywhere from 1.5 to 3 meters when stretched out entirely, and that is just one molecule. We usually use things like water molecules as a point of reference for size, measuring at about 0.275 nanometers in diameter if you are curious. Neither of these can correctly encompass a proper representation of the size of the transistors that the Peking University researchers have developed.

What we do know is that these switches are built from graphene (a molecular arrangement of carbon that is one atom thick) electrodes with methylene groups in between them. No media outlet has given us a proper clue of how large such a transistor would be, but it may be a safe bet that we’re looking at something closer to a water molecule (considering how small graphene and methylene groups are) than a DNA molecule.

Size Isn’t Everything

While it’s important to make sure you pack as much of a punch as possible within a small amount of space, reducing the size of transistors isn’t the only thing you can do. Along with making an effective molecular switch that has a significantly higher lifespan (one year) than its predecessors (a few hours), the researchers at Peking U. have also achieved another breakthrough: the switch can also communicate using photons rather than moving electrons. Photons travel much faster than electromagnetic waves do (up to 100 times faster), meaning that we’d be able to both cram more transistors into small spaces and give each of those tiny buggers a speed boost the likes of which Gordon Moore could only have ever dreamed of.

Why This Tiny Hardware Is Challenging

As with anything that we deal with on the atomic or molecular level, things can get very unstable. For example, electromagnetic fields have a strong tendency to cause the atomic structures of metals and other conductive materials to shift ever so slightly. Such a shift can be interpreted as a signal. Microscopic “grains” of material at the atomic level could also cause transistors to function improperly. The Peking U. researchers have managed so far to create a switch that could activate and deactivate over one hundred times, with a durability of one year. While this is a wonderful achievement as it stands, I doubt many people would be thrilled to have a computer with the lifespan of a cancer-prone hamster. The first real challenge is in isolating the micro-electronic environment in such a way that it can run for more than a decade.

Even if a viable, highly durable molecular switch is finally built by someone, getting this into a streamlined manufacturing process presents a whole new challenge on its own. For the foreseeable future, integrated circuits are the go-to method for internal hardware communication. Getting this bulky system to function with molecular switches is near-impossible. To add insult to injury, measuring things inside the tiny gaps between molecules (which you need to do to read the data stored inside) requires highly-specialized environments that need lots of energy to maintain.

The Takeaway

The endeavor of having switches the size of some of the smallest molecules mankind can manipulate is very tempting and holds lots of promise. That is, if manufacturers can get through hurdles such as requiring cryogenic temperatures to read data, getting rid of the gap in connectivity between molecules and caveman-level electromagnetic circuits, and somehow mitigating the tiny lifespan of this technology when put to the test in the real world. If they can jump through these hoops, then yes, molecular switch technology is certainly going to create a revolution that will completely render current integrated circuits and silicon-based chips obsolete.

Miguel Leiva-Gomez

Miguel has been a business growth and technology expert for more than a decade and has written software for even longer. From his little castle in Romania, he presents cold and analytical perspectives to things that affect the tech world.

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The Promise Of Bitcoin Is About Much More Than Financial Freedom

Imagine a game of chess where the pieces are all jumbled up. A player might be given four queens while their opponent has none or a score of bishops and rooks to match. Such is the premise behind “Really Bad Chess,” released in 2024 by developer Zach Gage. Although many have analogized chess to the world at large (viewing the game as a microcosm of society with its hierarchy, roles, and interactions among the pieces), what makes “Really Bad Chess” more fitting is that (as in the game) life doesn’t offer an equal starting point for everyone. 

Some players must work harder to “win,” leaving it up to their skills to compete with even the most privileged opponents. Moreover, people in society’s upper echelons (similar to those who start with an array of powerful pieces) are often reluctant to relinquish their positions. They may even attempt to control the choices of other players.

In essence, promoting a modest pawn into a formidable queen has become increasingly difficult. And no more pronounced is this in our digital age. Despite the widespread connectivity of the web and social media, tech and political moguls continually seek to manipulate societal systems to restrict, surveil, and control the liberties inherent to all human beings. 

That said, tools are emerging that promise users that they will help level the playing field and preserve these fundamental rights. One such tool is Bitcoin. When it initially launched, Bitcoin was intended to help people reclaim financial freedom. Today, it has transformed into an ecosystem of tools and products that promise to safeguard not only the freedom to transact but also the freedom of speech and the right to a free and uncensored culture.

Transactions and how Bitcoin works

Before Bitcoin, there were attempts to create an open, peer-to-peer financial system, like eCash, B-money, Bit Gold, and Hashcash. In 1984, Nobel Prize-winning economist Friedrich Hayek suggested a type of money that governments couldn’t control. And in 1997, the NSA published a paper on the cryptography of anonymous electronic cash. However, Bitcoin succeeded, whereas others failed by effectively solving the Byzantine Generals’ problem related to the issue of “double spending.” 

The Double Spend Problem refers to stopping digital money from being spent twice. Intermediaries like banks prevent double-spending in traditional financial systems by privately verifying each transaction. But with this centralized oversight comes the overt influence on monetary policies and regulations. Before Bitcoin, attempts at solving the double spend problem all had some semblance of a central administrator handling the books and, therefore, carried the risk of manipulating the system in one way or another.

Credit: elenabs/iStock

The Bitcoin Network differs by allowing any member involved to verify and agree on the validity of transactions. It achieves this through its proof-of-work consensus mechanism. Within this system, participants (called nodes) on a public network spend computing power (energy) to verify transactions and add the data to the blockchain. Here, “the blockchain” is essentially a buzzword to describe something akin to an immutable accounting system. Contributors (miners) are rewarded for maintaining and securing the network by earning Bitcoin, hence the analogy and phrase of “mining” due to bitcoins parallels with gold. 

Detractors claim that Bitcoin is primarily a “decentralized Ponzi scheme” or worse, “rat poison squared.” But proponents maintain that it offers a more resilient and open monetary system — one that counteracts the inflationary consequences of currency debasement, a technique used by governments to meet financial obligations, stimulate domestic spending on infrastructure, fund wars, and the like. 

How Bitcoin protects the freedom to transact

Take, for example, the hurdles of opening a simple savings account or obtaining loans — banks often demand extensive documentation and prerequisites. Conversely, Bitcoin offers an alternative to traditional remittances, providing a crucial economic lifeline for many developing countries and low-income people with free-to-use digital wallets. 

Furthermore, peer-to-peer (P2P) Bitcoin networks boast lower fees than some conventional platforms like Western Union, making them more appealing in emerging nations. For example, a Reuters report published in 2023 demonstrated how an African small business owner increased profits and safeguarded his venture from currency devaluation by compensating suppliers in Bitcoin. BTC had no added fees, and transactions were swifter, more secure, and cheaper than traditional money-transfer firms.

Even in developed countries, Bitcoin is not without its value. The recent mismanagement of Silicon Valley Bank aside, alternative financial systems have become increasingly necessary in light of the growing censorship and penalties imposed by payment platforms such as PayPal. In 2023, the company reversed a contentious policy that might have resulted in users being charged $2,500 for disseminating “misinformation,” as the payment platform asserted that the policy update had been released “by mistake.” Conversely, Bitcoin’s decentralized nature provides a way to conduct transactions openly without relying on highly centralized, authoritarian platforms. 

Prominent investors and entrepreneurs like Naval Ravikant and Paul Krugman have also highlighted Bitcoin’s potential as a hedge against financial collapse and a means of diversifying portfolios amid possible financial instability, not to mention its promise as a store of value. Bitcoin’s importance as a geopolitical force has also been evident in real-world crises like the Ukraine conflict. As recounted by Twitter user usleepwalker, who experienced the crisis firsthand, traditional banks were inaccessible during the turmoil, preventing people from accessing their funds. In contrast, those with cryptocurrency could rely on their assets for payment and alternative stores of value. 

Bitcoin and freedom of speech

Decentralized technologies like Bitcoin support freedom of speech by enabling users to embed and inscribe messages on its blockchain through resources like Ordinals, Bitcoin Stamps, and Nostr — all of which provide the ability to cryptographically preserve messages (regardless of the content) without gatekeepers or intermediaries in a decentralized public manner. 

The idea of filling Bitcoin blocks with JPEGs and videos — or even video games — isn’t sitting well with some in the Bitcoin community who have voiced concerns that putting NFTs directly on the Bitcoin network will drive up transaction costs.

Marginalized peoples in developing countries will have to pay more to run their Bitcoin nodes and send transactions because privileged wealthy whites want to put JPEG drawings on the blockchain as status symbols. Just because you can doesn’t mean you should.

— Bitcoin is Saving (@BitcoinIsSaving) January 29, 2023

But despite this divisiveness, such methods are another way and pathway or open door for anyone to preserve and share information for posterity in a censorship-resistant manner, free from the constraints of any central authority. 

Free culture 

For digital culture to flourish across various subgroups and niches, creators must be encouraged to experiment, allowing markets to respond organically to their work. However, as the boundaries between tech oligarchs and the platforms on which creators rely to innovate and monetize their work become increasingly restrictive (e.g., YouTube demonetizing videos or engaging in overt censorship), viable options within the web2 landscape are diminishing.

Fortunately, Bitcoin offers an array of alternatives (even before Ordinals) to help counteract the polished curated media and permissible boundaries of creative expression. Created in 2014, Counterparty is one example of a platform that allows people to create assets on Bitcoin like grassroots cryptographic artwork and memes (otherwise known as CryptoArt), notable amongst which were assets like Rare Pepes  — tokenized variations of the Pepe the Frog meme by Matt Furie which still command significant sums on secondary markets.

In fact, within a mere two years of the Rare Pepe project launching, 1,774 unique Rare Pepe cards were approved for admission into the grassroots project. The cards included numerous references to pop culture and political satire like Hillary Clinton, Donald Trump, and Putin-themed cards, with some rarer one-of-one cards selling for as high as $3.6 million as seen in a Sotheby’s auction in October 2023.

Other memetic ecosystems were also inspired by Counterparty and Bitcoin’s technology, like Doge and a related platform for minting assets on Dogecoin like DogeParty (a fork of Counterparty and Bitcoin). Both demonstrate how the innovative technologies behind Bitcoin and Counterparty have spurred the development of diverse platforms that cater to niche communities and interests.

But ultimately, be it Doge or Pepe, they serve as an illustration of a culturally significant movement that has arisen and flourished in the digital realm from the native currency of memes, cultivated and preserved through the decentralized power of Bitcoin rather than being shaped and curated by traditional media outlets.

While initially focused on financial sovereignty, the Bitcoin ecosystem has evolved to protect a broad range of liberties, including transactional freedom, free speech through Ordinals, and an uncensored cultural platform via Counterparty and Nostr. 

What Are The Challenges To Iot Analytics Success?

The Internet of Things (IoT) is the most recent development with which various devices connected & communicate with one another over the Internet. These devices generate massive amounts of data that can be analyzed to derive insights & make informed decisions. But do you know IoT analytics success also has many challenges?

In this article, we will explore some key challenges to IoT analytics success.

Key Challenges to IoT Analytics 1. Data Integration and Management

One of the biggest challenges to IoT analytics success is data integration & management. IoT devices generate data in various formats & protocols, making it difficult to integrate & manage the data. It is difficult to adequately interpret, store & manage the data created by IoT devices because of its sheer amount and diversity. To guarantee that the data is precise, consistent, & secure, organizations must have strong data integration and management systems in place.

2. Data Quality

Data quality is another significant challenge to IoT analytics success. The data generated by IoT devices is often incomplete, inaccurate, or inconsistent. It is because IoT devices may malfunction or experience network connectivity issues, leading to data loss or corruption. Furthermore, IoT devices may generate data that is irrelevant to the analytics process, leading to noise in the data. To guarantee that the data used for analytics is correct, consistent, and relevant, organizations need to invest in data cleansing & validation processes.

3. Security and Privacy

IoT devices produce many useful yet sensitive data for an organization. The data may contain much in-depth information also about the company. But as it is always connected to the internet there is a huge issue of security. To guarantee that the data is secure and secret, organizations must implement strong security & privacy measures.

4. Scalability

IoT analytics involves processing and analyzing massive amounts of data in real-time. It takes a large amount of processing and storage power. Organizations may find it challenging to meet the demand for computational power and storage space as they increase their IoT implementations. Organizations need to have scalable analytics platforms in place to handle the growing volume of data generated by IoT devices.

5. Interoperability

It means the ability of various devices communicates with one another. Interoperability is crucial in the context of IoT analytics because IoT devices may produce data in different forms and protocols. To guarantee that the data is integrated & analyzed properly, organizations must have systems in place that can handle various data types and protocols.

6. Lack of Skilled Workforce

IoT analytics requires a skilled workforce that can analyze and derive insights from the massive amounts of data generated by IoT devices. But there is a lack of knowledgeable data scientists and analysts who can work with IoT data. Organizations need to invest in training and development programs to build a skilled workforce that can handle IoT analytics effectively.

7. Data Governance

Data governance refers to the management & control of data assets in an organization. Data governance is crucial in the context of IoT analytics to make sure that the data is appropriately managed and used by legal standards. To ensure that the data created by IoT devices is managed successfully, organizations must have strong data governance frameworks in place.

8. Real-time Analytics

IoT devices generate data in real time. Real-time data analysis is necessary for every organization that wants to make educated judgments. Real-time analytics can be challenging as organizations need to process & analyze data in real-time to derive insights. All Organizations must have real-time analytics platforms in place. This real-time analytics platform can handle the volume & variety of data IoT devices generate.

9. Cost Implementing

IoT analytics can be expensive, as it involves investing in hardware, software & skilled manpower. Organizations need to consider the cost-benefit analysis before implementing IoT analytics. They need to weigh the benefits of IoT analytics against the cost of implementation to ensure that it makes business sense to invest in IoT analytics.

10. Standardization

Standardization is another challenge to IoT analytics success. You won’t find any standardized protocols or frameworks for IoT devices. So, it is hard for organizations to integrate & manage the data IoT device generates. This lack of standardization causes many challenges for organizations to scale their IoT deployments. Also, create challenges to making informed decisions based on the data generated by IoT devices. Standardization efforts need to be made to ensure that IoT devices can communicate with each other seamlessly & the data generated by these devices can be integrated & analyzed effectively.


IoT analytics can transform various industries, from healthcare to manufacturing. It can take any industry to the highest point. However, there are significant challenges to IoT analytics success that organizations need to overcome. By addressing these challenges, organizations can unlock the full potential of IoT analytics and derive insights that can inform decision-making and drive business growth.

Iphone 4S Up Close And Personal: Hardware

iPhone 4S Up Close and Personal: Hardware

In this chapter of our “I’m switching to iPhone 4S for a week” experiment, we’ll be taking an up close look at the hardware that makes up the iPhone 4S. When I say up close, I mean macro photography close – so close you can see the tiny specs of dust and micro-grooves in the metal close. What you’re about to see will prove to you, beyond any shadow of a doubt, that the iPhone 4S is not just another smartphone, not just another hunk of plastic, metal, and glass that so many manufacturers toss out on what seems to almost be a weekly basis – the iPhone 4S is a precision built gadget masterpiece.

This smartphone came into my hands more than once before this week began, the iPhone 4 model being essentially the same I can safely say that I’ve seen and played around with this particular iPhone iteration in the past. This week represents your humble narrator’s first real extended hands-on look at the iPhone 4S. Here we’re looking at the device so close that the human eye wouldn’t likely have picked up such a collection of nuances. To get this close to a device is to know it all too well, and not many devices would hold up to such scrutiny.

At the top of the device is the power button, also functioning as the lock button when you’d like to shut the display off without powering the device down entirely. This button is one of several that sticks above the fold, just enough that you’re able to feel it with your finger to activate or de-activate your phone with ease. Similarly the two volume buttons and the screen orientation lock/volume lock switch are raised about the metal surrounding the device at precisely the same level. The volume buttons are marked with as simple a set of icons as possible: one + and one -, while the volume lock/screen orientation lock is marked only when activated by a small orange embedded mark.

The power button on the front of the device is depressed so that it allows your thumb to move into it with ease, and pressing the button does not reveal any open spaces even though it does remain a physical, moving piece of the machine. The square here remains another one of the simplest marks in the mobile world, a rounded-corner nondescript shape which signifies a return to the source from wherever you are in the device’s interface.

Near the home button at the bottom of the device is the Apple connector port in the middle of two screws and two speaker grills. These grills are below the surface of the metal and, along with the port for the Apple connector, are amongst the biggest dust-attractors on the device. These areas are ripe for collecting the nastiest bits of grime from your pocket and make a great case for picking up a cover for your device lest its beauty be lost to your own daily use of it. The close-up shots you see in this post were taken after I did a basic cleaning of the whole device – getting rid of the entirety of the dust on this device is essentially impossible.

The glass on the front and the back of the device is not just a flat piece cut in one – it’s got curves around its corners, rounded edges around their entirety, and have an added bump and platform before they go flat. This device is the first to take the number one flag away from my favorite glass treatment on a device of all time: the LG G2X. The curves on the glass of that device are nice, here the precision is completely unmatched.

The back of the device has the Apple logo, some information on the design and model, and all of it sits below the glass in reflective chrome printing so that it’ll never, ever get worn off. Also below the glass is the camera’s flash, and surrounded by a tiny silver ring next to the flash is another individually cut piece of glass in front of the camera. The camera itself is highly visible even without a macro view – but take a look at that perfect set of rings!

Up top of the device where we began this journey you’ll find one microphone hole and one headphone jack. When you get in close enough to see the details in the rings, you’ll find that each hole has been cut with tools so small it’ll make your eyeballs spin. Tiny instruments for perfect little holes.

There’s nothing like this in the smartphone world right now outside the iPhone 4S. This is but one of our explorations of the device, the display, the software, and the rest sitting in the rest of our week of hands-on looks at this device. When you hold an iPhone 4S, you know that it sits in a Sweet Spot, perfectly suited for an adult hand, your thumb able to access the entirety of the display without you needing to hold the device aside with two hands, the bulk of the device heavy enough that you know it’s high quality, not too light that you’ll accidentally toss it out with the newspaper.

This device is smaller than the bulk of the high-end smartphones on the market today, and Apple’s competitors have made no secret about touting that fact. A lesser company would falter under the pressure of having one phone size throughout their history – Apple will more than likely keep this size until the lines go down at the stores. The iPhone remains the world’s most popular smartphone for years and years running not least of all because its outer hardware is so precise. Follow the rest of this “I’m switching to iPhone 4S for a week” in the timeline below, and stick to the SlashGear main news feed for updates as they appear!

An Overview Of Iot Sensor Types And Challenges

In the design of most IoT gadgets, sensors play a central role. Internet of Things product development revolves around sensors. Environmental changes can be detected and responded to with the use of sensors. They gather information for smart devices to use and adjust. For sensing purposes, sensors can be attached to various objects and machinery. IT personnel need to be familiar with the different kinds of IoT sensors, the data gathering process, and the risks associated with hardware failures and security to effectively manage and support IoT implementations. for example, a sensor picks up on this change and translates it into an electronic signal. Instrument producing a useful result in response to a certain input parameter. The signal is transformed into a form that can be read and utilised by either human beings or machines. Environmental inputs from motion and pressure changes are among the sources of information that they get.

IoT sensors types and their overview

All sensors are placed at the network’s front end to collect information through IoT networks. Sensing input on one’s own, as with an active sensor. The function of a sensor dictates its design, and a passive sensor is dependent on another source for its data. (Ex- temperature, gas, strain, colour, and smoke detectors).

Sensors, for instance, can operate in digital or analogue modes. A digital sensor gives a binary response. Direct communication between a digital sensor and a microprocessor in an IoT device. It has additional circuits for bit conversion in addition to the analogue sensor. Data collected by an analogue sensor will need to be converted to a digital format, adding an extra step if the application requires an analogue sensor.

Non-contact analogue methods are utilised by distance or range sensors. When broken down even further, sensors can be categorised by their tasks. Transmitted energy waves, such as radio waves, sound waves, and lasers, are used for sensing at greater distances. Some sensors can monitor changes in pressure in sealed settings like those found in automobiles, aircraft, factories, and labs. They are most commonly employed as proximity sensors. Still, they can also be used for range sensing, which involves determining how far away or close a component is to the sensing site.

Reporting data and obtaining data process

It is up to IoT developers to determine how sensors collect and transmit data. A sensor’s output is proportional to the value of a given input.

Sometimes the information gathered by sensors is in a binary form. Drift refers to how much a sensor’s readout varies from a set value after being held constant for a long time. Sometimes, information takes the form of text. Although any acceptable mechanical or electrical switch could be used, micro-switches are typically employed due to mechanical switches’ relatively high force requirements. Some sensors, on the other hand, gather analogue data that must be transformed into a digital format before a network and its associated applications can process it.

When an input parameter is a vector, the sensor’s output is proportional to the input’s strength and direction and orientation. When data is ready for analysis and outputs like alarms, the network sends it to the cloud or an on-premises processing engine.

Challenges that IoT sensors face

One of the major IoT security concerns is a lack of encryption, even though encryption is an excellent way to prevent hackers from accessing data. To ensure the smooth functioning of end-to-end IoT systems, businesses need to restructure their staff. Sensors are a part of this. Most companies have IT departments responsible for setting up and maintaining IoT devices. As a result, assaults in which hackers readily subvert security algorithms have increased.

Five problems that IT Departments have to Overcome

Low battery life − Sensors are susceptible to failed battery operation. Designers also need to find a solution to the design time challenge and release the embedded device at the optimal time. Even though it helps extend the life of batteries, this doesn’t make battery replacement unnecessary.

Failing sensors − These drives mimic the processing power and storage space of a conventional computer. When Internet of Things (IoT) sensors deployed on-premises experience problems, they can be quickly swapped out by IT departments. However, the widespread deployment of sensors in the field increases complexity. Sensors that have stopped working can be fixed in one of two ways: by replacing them or by properly accessing them. In this case, the processing or value of the data saved is more important.

Security − There are new security concerns associated with the Internet of Things and the gadgets and sensors it includes. The lack of properly skilled personnel working on IoT application development poses a significant threat due to the aforementioned development issues. For instance, the majority of companies market their products with the same factory-default settings and passwords. Especially at the network’s periphery or in IoT deployments, this can open a serious security hole. Systems must be built with security in mind, using cryptographic algorithms and other safeguards to prevent unauthorised access. Incoming sensors and IoT devices require reprogramming by IT to meet corporate security and governance requirements. Without this, malicious people can easily enter.

Noise pollution − When attempting to link hardware, software, and cloud infrastructure, connectivity is always top of mind. Some sensors have erratic performance in various settings. Problems with the packing and integration of lightweight, low-power-consumption small-sized chips are limiting the battery life of portable electronics. Car safety features like forward collision warning and automated emergency braking can be rendered useless if ice and snow accumulate on their sensors during the winter months.

Uneven data and connection standards − Data collection, storage, and processing within an environment all require careful planning by development teams to maintain security and privacy. There is a great deal of variety in the kind of information collected by sensors and the types of communication standards used by sensors. IoT applications need to be built with cross-platform compatibility in mind as new technologies emerge in the future. Since IT teams already have gateways and networks in place, integrating sensors into those might be difficult.


Top 5 Sentiment Analysis Challenges And Solutions In 2023

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.


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.”


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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