Sentiment analysis is the process used in analyzing online pieces of writings to determine the emotional tone in them. The vibrant tones can either be negative, positive, or even neutral. This article will help you understand how to make the most of sentiment analysis. Here are six of the best practices that can improve the performance and accuracy of your sentiment analysis.
1. Providing domain-specific features in the corpus
Careful selection of the test and training corpus are essential when you are carrying out sentiment analysis. It is considered crucial because domain knowledge plays a critical role that helps various features act in your analysis.
For instance, if the issue is “social media monitoring Indonesia,” your training corpus must contain valid data from social sources like Facebook and Twitter. On the other hand, if the problem is “sentiment analysis Bahasa Indonesia news,” your corpus must feature valid data from various reliable news sources.
2. Using a noise-free corpus analysis algorithm
Compared to a noisy corpus, a clean corpus analysis algorithm is more preferable in most data science problems. A noisy corpus usually includes all the outside entities of the text and may consist of numerical values, punctuation marks, URLs, or links. By removing these entities from a text, you can increase your data accuracy due to a decrease in the sample space’s possible features. However, you may only exclude these entities if your analysis problem does not initially use them in the first place.
3. Using exhaustive stopword lists
Stopwords are the widely used words in the corpus. The most common ones include “of, a, the, on… etc.”. These words define the structure of a sentence but have no use in the context definition. Treating them as feature words would lead to poor performance in the sentiment analysis. It would be better to ignore these words in the text corpus if you want to achieve a better understanding.
4. Eliminating keywords with a lesser frequency
Keywords that are occurring in a lesser frequency in the corpus essentially play no roles in the analysis. For better performance, you should consider ignoring such words. For instance, if the minimum threshold of frequency counts for both the terms “social media monitoring Indonesia” and “sentiment analysis Bahasa Indonesia” is at 11, all other keywords that occur at a lower frequency than 11 can be ignored, thus improving the accuracy.
5. Using a normalized corpus
Words play a critical role in analysis techniques. Words are also used in many different variations in the text depending on its grammar. Therefore, it is always essential to normalize the terms to their root forms to avoid inaccuracy.
6. Using complex features
A combination of words as features, in some cases, provides a better significance compared to single words. Additionally, to get an extra set of feature space, you can consider combining one of the speech tags with some words.
And that is how you can make the most of sentiment analysis. Using the above-mentioned best practices will give you an improvement of 10% to 20% when it comes to accuracy, depending on how you view it in your case.
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