Sentiment Analysis Challenges in NLP: A 101 Solution Guide

Sentiment analysis is a natural language processing (NLP) technique used to analyze online data and categorize them as neutral, negative, or positive. The process uses machine learning algorithms to evaluate textual data and detect their underlying emotions like joy, satisfaction, frustration, or disappointment.

Sentiment analysis is mostly used by brands to monitor product and brand-related sentiment in customer feedback or discussions on public platforms. It helps them gain insights into how people feel about a specific product, brand, or content.

Thus, sentiment analysis helps detect potential risks and product or content downsides and informs decision-making based on public perception.

Why is Sentiment Analysis Tricky?

Sentiment analysis challenges include interpreting the meaning of words in different contexts. AI often struggles with analyzing different sentiments, such as sarcasm or negation, or sentences with different sentiments. In such cases, it becomes difficult to categorize the texts as positive, negative, or neutral.

For example, a sarcastic comment like ‘Yeah, awesome. They took three weeks to deliver my order.’ Similarly, a review with multiple sentiments (multipolarity) like, ‘I am happy with the service but not the product pricing’ or phrases with negations and double negatives like ‘not bad’ can be confusing for AI systems to interpret correctly.

Another challenge in sentiment analysis is the contextual understanding of words. The sentiment of certain words can change based on how they are used.

For example, words like ‘kill’ can be positive or negative depending on the context. For example, ‘your customer support is killing me’ expresses a negative emotion, while ‘the brand’s launched spicy wings are killing it with their flavor’ expresses a positive sentiment.

Emojis, Slang, and Abbreviations: The Emoji Dilemma

According to Statista, the face with tears of joy was the most used emoji (480 million times) across Reddit and X (formerly Twitter). Interpreting emojis is among the most common sentiment analysis challenges, along with abbreviations and slang.

Emojis make it complex for AI systems to understand the sentiment behind a comment due to their context-dependent interpretations. One emoji might carry different emotions based on how it is used in the review or comment. Hence, it gets tedious and complicated for AI systems to interpret emoticons effectively.

For example, comments like ‘Received the product in 2 days🥳#deliveryservices’ or ‘The product is 🥰’ are more visually expressive. Without the emojis, the comments might be categorized as neutral instead of positive in sentiment analysis. Similarly, emojis like 🥹or 😂can represent laughter, joy, or sarcasm based on the context, making it challenging for AI to understand.

Interpreting slang and abbreviations is another sentiment analysis challenge, as they complicate the analysis with unique interpretations. For instance, ‘ TBH, Amazon’s delivery is lit🔥’ requires understanding the slang term and the abbreviation for accurate interpretation, which can be challenging for AI systems.

Short vs. Long: Why Text Length Matters?

Short texts or tweets often lack sufficient context for sentiment analysis, making it a struggle for the algorithms to understand the intended sentiment. It is often challenging to grasp the complete emotional tone in a short text. Similarly, short texts like ‘The product is 😍’ introduce ambiguity, increasing the risk of misinterpretation of the intended emotion.

Longer texts like ‘The product is sturdy, but the color could be better, and the delivery services definitely need an improvement’ convey both positive and negative emotions.

Sentiment analysis algorithms struggle to grasp the mixed sentiment in this review, leading to inaccurate interpretations. Balancing the length of the texts is crucial to overcoming sentiment analysis challenges.

Speaking Globally: Sentiment Analysis in Different Languages

Multilingual sentiment analysis is crucial for businesses with an international customer base because sentiment can drive user action, and speaking native languages triggers more emotions.

According to Stillman Translations, language influences consumer perception. To understand how linguistic cues impact consumer preferences, English brand names were translated into Chinese. Considering the native languages in brand naming aligns the brand with customer perception and expectation, ultimately impacting their buying behavior.

Organizations with an international customer base face multilingual sentiment analysis challenges. They include:

1. Machine Translation Limitation

Sentiment and emotion cannot be accurately interpreted by machine translation. They cannot detect sarcasm or irony as it requires understanding the context. However, if the analysis requires basic sentiment information in different languages, sentiment analysis tools that use machine translation can detect sentiment from translated text effectively.

2. Additional Pre-processing with Different Encoding

Texts encoded in different formats are challenging in multilingual sentiment analysis. Languages with non-Latin alphabets or older texts may have different encoding styles, requiring extra pre-processing steps for an accurate and effective analysis.

3. Training Needs

Sentiment analysis tools are primarily trained to analyze words and phrases in one language, typically English. When the analysis involves multiple languages, the tools might lack language-specific training data, leading to inaccurate and unreliable results.

Real-Life Twist: Sentiment Changes Over Time

Sentiment in texts evolves due to certain factors, like emerging trends or changing societal norms. Analyzing the change in sentiments over time is critical to understanding and keeping up with the shift in opinions and public reactions.

Here are a few challenges to updating sentiment analysis models with evolving trends:.

1. Contextual Relevance

Sentiment analysis models must be updated with new slang, abbreviations, and expressions as they constantly evolve. The models must adapt to these linguistic updates to overcome the challenges in sentiment analysis.

2. Data Relevance

The data keeps changing with changing customer perceptions. For example, a brand might experience a shift in customer perception due to a controversial issue or a social campaign. Similarly, product reviews can also change over time. Hence, models must be updated with relevant datasets.

Hidden Meanings: Detecting Sarcasm and Irony

Irony and sarcasm require AI systems to understand the contextual meaning. Further, it gets difficult to categorize sarcasm or irony as positive or negative. Here’s why:

1. Opposite Meanings

Sarcasm creates a discrepancy between the literal words and their meaning as it involves saying something and meaning its opposite. For example, ‘Oh great! Another fast delivery!’ with a tone of frustration due to delivery delays can easily confuse sentiment analysis models.

2. Contextual Dependency

Detecting irony and sarcasm heavily depends on nonverbal cues, tone of voice, and the surrounding context. This makes it challenging for sentiment analysis models to interpret them accurately.

3. Language Complexity

There are different ways of expressing sarcasm and irony, like understatement or exaggeration. These diverse expressions and their complex meanings, different from the literal words, are hard to interpret by sentiment analysis models. For example, ‘We have another great product by XYZ’ during a bad product experience means the opposite of what has been written by the user.

Bias Alert: Why Sentiment Analysis Can Be Unfair?

Sentiment analysis models can inherit biases present in the training data. The results will be biased if the training data is skewed towards specific sentiments or opinions. Also, the sentiment models trained in specific cultural contexts or specific languages struggle to interpret sentiments expressed in different languages or cultural references. This may decrease the accuracy and fairness of the outcomes.

Here's why it is important to address bias in sentiment analysis models:

1. Increased Accuracy

Addressing biases in training data improves the reliability and accuracy of the outcomes. Using different normalization techniques, biases can be addressed to get more precise insights into customer opinions and perceptions in sentiment analysis.

2. Ethical Implications

Addressing biases that might generate discriminatory or misinformed interpretations is crucial to maintaining ethics in sentiment analysis. Addressing biases will also help ensure the fair treatment of users represented in the analyzed texts.

3. Transparency

Addressing bias increases the transparency of sentiment analysis, ensuring trust among stakeholders in the process. Mitigating the biases also ensures fairness and integrity in the sentiment analysis.  

Real-Time Roadblocks: Challenges in Quick Analysis

There are sentiment analysis challenges that occur when implementing sentiment analysis in real-time apps.

1. Time Sensitivity

Real-time analysis requires instant processing of the incoming data to provide insights. Thus, the algorithms must be efficient enough to quickly analyze sentiments without affecting the quality of the results.

2. Changing Data

The data in real-time apps changes constantly based on current trends, changing customer behavior, perceptions, user interactions, etc. This requires the models to adapt to these dynamic shifts to offer up-to-date information.

3. Data Volume

One of the main challenges in sentiment analysis is handling a growing data volume. Rapid data scaling requires capable models that can manage high-velocity incoming data without delays.

Here’s why there is a need for models that quickly adapt to changing data.

A. Real-time Decisions

Data is constantly changing in industries like healthcare, finance, and technology. This requires models to adapt to these evolving data to provide relevant and up-to-date outcomes.

B. Enhanced Performance

Flexible models that quickly adapt to changes showcase improved performance and reliability in dynamic environments. Updating models with new information, parameters, etc., can increase their effectiveness in analyzing complex datasets.

Relevant Results

Adaptable models generate up-to-date outcomes that remain relevant over time. This adaptability allows models to capture changing customer sentiments or behaviors to reflect the current situation.

Beyond Good and Bad: Handling More Than Two Sentiments

Multi-class statement analysis is challenging due to the complexity of languages and the struggle to understand contexts and quantify them based on how users express their feelings. Here are the challenges in multi-class sentiment analysis:.

1. Biased Datasets

Multi-class sentiment analysis is more prone to biases, as certain sentiment classes might be underrepresented. Such skewed distributions can lead to biased and inaccurate sentiment analysis outcomes.

2. Contextual Interpretation

Multi-class sentiment analysis requires the models to understand the context and language nuances of the texts. Since several emotional states are expressed in a text, it is complicated.

3. Neutral Sentiment Interpretation

Multi-class sentiment analysis involves interpreting mixed sentiments. Thus, the models are required to distinguish between negative, neutral, positive, and ambivalent sentiments that do not indicate a specific classification.

Conclusion

Sentiment analysis challenges primarily include contextual understanding, interpretation of emojis and slang, handling diverse sentiments, and addressing biases without compromising on outcomes.

To overcome the challenges in sentiment analysis, you can upgrade to flexible models, use data cleansing and normalization techniques, balance sentiment classes, and train models for improved contextual understanding.

Moreover, you can enhance the quality of your datasets using tools like MarkovML. The feature-rich platform can streamline your data, simplify data analysis, and help enhance model accuracy. To learn more, connect with us today!