Preprocess the Text Based Customer Review Data for Sentiment Analysis

Sentiment Analysis is commonly referred to as opinion mining and it is a fast-developing area of text mining and Natural Language Processing. To ascertain the author’s sentiment, computer analysis of textual data is required. Sentiment analysis seeks to automatically categories a text’s sentiment as either Positive, Negative or Neutral. In recent years, sentiment analysis has drawn a lot of interest as a useful technique for obtaining and analyzing consumer thoughts and attitudes from textual data. Before using machine learning techniques, preprocessing is essential for text-based evaluations since it helps to clean, transform, and prepare the raw text data into a format that machine learning models can use efficiently. In this research work, thorough investigation of sentiment analysis methods is applied to remove the unwanted textual information through preprocessing techniques. For that, the electronic product review data have been collected from the publicly available dataset. The main goal is to apply some existing pre-processing techniques like stemming, tokenization, stop-word removal, word lemmatization, punctuation and symbol removal, Upper to lower conversion of text, vectorizer in feature extraction methods in order to clean up the raw data and prepare it for further sentiment analysis on electronic product reviews. In this research work, there is no proposed preprocessing methods were applied. Finally, this work suggests to use further processing to find the sentiments of taken data.