Simply put, sentiment analysis is nothing but figuring out whether the piece of article or writing can be categorized under positive, negative, or neutral. This categorization helps the flow of audience and opinions on to space. However, there are many things you must be more concise while we check on the sentiment analysis. Over the years, the sentiment analysis has found a special place in the people’s and website developers’ heart. This sentiment analysis is based on analyzing many factors, and they are social media, datasets, visualizations, and even the evaluation methods as well. Without creating any further ado, let us hit the post and understand what the main things that classify the sentiment analysis are.

Social Media

Like mentioned before, sentiment analysis read the input texts and figure out whether they fall under positive, negative, or neutral. The sentiment analysis helps you to study and review the texts and posts that are uploaded by the reviewers and users. These posts may be reviews about anything. It might be regarding the product review, or services, or even the person. The sentiment analysis platform has tools to identify which tone the posts are written. This is done with the help of NLP, Natural Language Processor.

Social Media

What to do before starting the sentiment analysis?

There are various things one must do when they carry the activity of sentiment analysis, and some of them are data sets and pre-processing. The datasets will be provided for various social media contents, especially Twitter and Amazon posts. There are some of the famous datasets in the sentiment analysis you may not know, and they are none other than Stanford Twitter Sentiment, Amazon Reviews for Sentiment Analysis, Sanders Corpus, Sentiment Strength Twitter Dataset, and lastly SemEval (Semantic Evaluation) dataset. The latter one is the pre-processing platform. Here, this step is considered as the primary step to the analysis. There are a lot of steps that are included below the pre-processing steps, and they comprise of Remove numbers, Part of speech tagging, Lowercase, Stemming, Remove punctuation, and lastly Remove stopwords.


Classification of Sentiment

There are three types of classification of sentiment analysis, and they are Machine Learning, ML, Lexicon-Based, and lastly Hybrid. We will briefly explain them in the following points.

Machine Learning: it is the method of technique helps the owners and people to identify the text and posts and present it before the data to classify it.

Lexicon-Based: the second one is the Lexicon-Based, here; the technique is used to classify the variety of sounds by differentiating and understanding its polarity score.

Hybrid: the last one on the list is a blend of the points above. However, this is known to bring the actual results than the other two mentioned above.

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