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Survey Paper on Sentiment Analysis: Techniques and Challenges

EasyChair Preprint no. 2389

4 pagesDate: January 15, 2020


Process of finding out extracting experiences and emotions from the given dataset is called Sentiment Analysis. It is also called as Opinion Mining. By using sentiment analysis on the reviews the customer and enterprises can big a major change in the decision making process. There are different methodologies while making a sentiment analyzer. Data acquisition, data preprocessing and training with an algorithm are some of the steps involved in the methodology. There are various challenges while making a sentiment analyzer. In this paper we are going to survey  different steps and techniques on sentiment analysis. We also studied previous work and tried to compare them and find out a better way to increase the accuracy and efficiency of a model. Naive Bayes and Support Vector Machine are mostly used classifiers.  Further we discuss various challenges in sentiment analysis.

Keyphrases: Lexicon based approach, machine learning, Naïve Bayes (NB), Opinion Mining, product reviews, Sentiment Analysis, Support Vector Machine (SVM)

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ansari Fatima Anees and Arsalaan Shaikh and Arbaz Shaikh and Sufiyan Shaikh},
  title = {Survey Paper on Sentiment Analysis: Techniques and Challenges},
  howpublished = {EasyChair Preprint no. 2389},

  year = {EasyChair, 2020}}
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