Sentiment analysis is the study of people’s emotions and opinions using natural language processing (NLP) cues. It is also referred to as “opinion mining.” The analysis tools can detect the meaning of slang and emojis to identify the message behind them. With the data from sentiment analysis, you can improve customer experience and develop better products.
Sentiment analysis tools are also available for download from app stores. But if you’re a software engineer, you can build a sentiment analysis tool using programming languages, such as Java or Python. You can also start learning sentiment analysis with Python online and improve your programming skills.
As with any technology based on NLP and emotions, there are some aspects of sentiment analysis that are difficult to determine. This article discusses some of the challenges that affect accuracy when doing sentiment analysis.
4 Challenges Affecting Sentiment Analysis Accuracy
Below are some of the challenges that have to be considered when conducting sentiment analysis.
1. Multipolarity Challenge
Multipolarity refers to the fact that text could have several opposing ideas, which could pose a significant challenge. For instance, customers may praise a certain product because of its positive qualities. At the same time, they may also criticize it because of other features that aren’t pleasing. In such a case, it’d be difficult to perform a complete sentiment analysis because the customer’s statement may contain some missing information. The sentiment analysis results can be misleading and may not be a true representation of the opinion behind the text.
Because of the two contrasting points, some models may attach positive or negative polarity to the text. However, to deal precisely with this aspect, an accurate analysis model needs to attach a polarity to each. For sentiment analysis tools, it might be difficult to assign two different perspectives.
2. Negation Detection Challenge
In ordinary language—the opposite of formal language—you can use negation to reverse the polarity of phrases, words, and sentences. Different rules can be used to know whether there’s a negation. You also need to be aware of the scope of words that affect the negation.
Most sentiment analysis strategies flag negation using a list of words that appear with a punctuation token. However, the negation can change depending on the context of the construction language.
You need to have several samples with different categories of negations to improve the quality of your data set. That would ensure accurate training and testing of sentiment classification models.
There are several cues that sentiment analysis tools use to detect negation. Here are some of them:
- Morphological cue using a prefix, such as “dis-” and “non-,” or a suffix, such as “-less”
- Explicit negation like “this is bad”
- Implicit negation, such as “with this attitude, it’ll be your first and last permission”
3. Sarcasm Detection Challenge
It’s not easy to detect sarcasm because sarcastic statements tend to use positive words. Sentiment analysis models find it hard to identify such implications unless the tool is designed to take the possibility of sarcasm into account.
Sarcasm appears a lot in the comments section of Twitter, Facebook, Instagram, and other social media sites. To identify them, you have to be aware of the context of the discourse, the topic of discussion, and the environment in which it’s used. Sarcasm isn’t only difficult to identify with sentiment analysis tools but also with people. There’s no single way of constructing sarcastic statements, which means it’s difficult to train a sentiment analysis model to understand sarcasm.
In natural linguistics, sarcasm is divided into four types:
- Illocutionary
- Propositional
- Like-prefixed
- Embedded
Researchers use different approaches to detect sarcasm automatically. The different approaches include rule-based, statistical, deep learning, and machine learning (ML) algorithms.
The deep learning approach is gaining more traction with the development of the deep learning model, namely, the CNN-LSTM-FF architecture. This model is perceived to be better than earlier approaches, as it improves the top level of accuracy in identifying numerical sarcasm.
4. Word Ambiguity Challenge
Another problem that sentiment analysts encounter is word ambiguity. It’s difficult to assign polarity scores to ambiguous words because they depend on the context of the sentence itself. To try and solve this challenge, analysts sometimes use the lexicon-based sentiment analysis approach, which uses opinion dictionaries that contain a collection of opinion words and their equivalent polarity values. Some lexicons are available online, such as those created by SenticNet, SentiWordNet, and General Inquirer.
Wrapping Up
Sentiment analysis is a complex field of study, as words take on different meanings when used in different contexts. Thus, it isn’t easy to decipher individuals’ emotions, as sentiment analysis tools may not always be able to identify the context in which particular phrases are used. However, with improved and more intelligent sentiment analysis models, it might be easier to accomplish more accurately in the future.
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