Sentiment analysis: a review and comparative analysis over social media

TLDR: This paper presents comprehensive overview of sentiment analysis technique based on recent research and subsequently explores machine learning (SVM, Navies Bayes, Linear Regression and Random Forest) and feature extraction techniques (POS, BOW and HASS tagging) in context of Sentiment analysis over social media data set.

Abstract: Sentiment analysis is the computational examination of end user’s opinion, attitudes and emotions towards a particular topic or product. Sentiment analysis classifies the message according to their polarity whether it is positive, negative, or neutral. Recently researchers focused on lexical and machine-learning based method for sentiment analysis of social media post. Social media is a micro blogger site in which end users can post their comment in slag language that contains symbols, idioms, misspelled words and sarcastic sentences. Social media data also have curse of dimension problem i.e. high dimension nature of data that required specific pre-processing and feature extraction, which leads to improve classification accuracy. This paper present comprehensive overview of sentiment analysis technique based on recent research and subsequently explores machine learning (SVM, Navies Bayes, Linear Regression and Random Forest) and feature extraction techniques (POS, BOW and HASS tagging) in context of Sentiment analysis over social media data set. Further twitter data-sets are scrutinized and pre-processed with proposed framework,which yield intersecting facts about the capabilities and deficiency of sentiment analysis methods. POS is most suitable feature extraction technique with SVM and Navie Bayes classifier. Whereas Random Forest and linear regression provide the better result with Hass tagging.