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Sentiment Analysis: A Comparative Study On Different Approaches…
Sentiment Analysis: A Comparative Study On Different Approaches
Information
Sentiment analysis (SA) is an intellectual process of extricating user’s feelings and emotions. It is one of the pursued field of Natural Language Processing (NLP)
The evolution of Internet based applications has steered massive amount of personalized reviews for various related information on the Web. These reviews exist in different forms like social Medias, blogs, Wiki or forum websites
Levels of Analysis
Document level
The main task is to classify whether a whole opinion document expresses a positive or negative sentiment
This level of analysis assumes that each document expresses opinions on a single entity
Sentence level
To check whether each sentence expressed a positive, negative, or neutral opinion
Closely related to subjectivity classification, which distinguishes objective sentences that express factual information from subjective sentences that express subjective views and opinion
Aspect level
Performs finer-grained analysis. Instead of looking at language constructs (documents, paragraphs, sentences, clauses or phrases), aspect level directly looks at the opinion itself.
Approach
Machine Learning
First trains the algorithm with some particular inputs with known outputs so that later it can work with new unknown data [2]. Some of the most renowned works based on machine learning are as follows:
Approach
Support Vector Machine
N-gram Sentiment Analysis
Naïve Bayes Method
Maximum Entropy Classifier
K-NN and Weighted K-NN
Multilingual Sentiment Analysis
Feature Driven Sentiment Analysis
Machine learning strategies work by training an algorithm with a training data set before applying it to the actual data set
Rule Based Approach
Defining various rules for getting the opinion, created by tokenizing each sentence in every document and then testing each token, or word, for its presence
If the word is there and has with a positive
sentiment, a +1 rating was applied to it. Each post starts with a neutral score of zero, and was considered positive
If the final polarity score was greater than zero, or negative if the overall score was less than zero [12]
After the output of rule based approach it will check or ask whether the output is correct or not. If the input sentence contains any word which is not present in the database which may help in the analysis of movie review, then such words are to be added to the database.
Lexical Based Approach
Work on an assumption that the collective polarity of a sentence or documents is the sum of polarities of the individual phrases or words
This method is based on emotional research for sentiment analysis dictionaries for each domains
Next, each domain dictionary was replenished with appraisal words of appropriate training collection that have the highest weight, calculated by the method of RF (Relevance Frequency) [15]
The word-modifier changes (increases or decreases) the weight of the following appraisal word by a certain percentage. Word-negation shifts the weight of the following appraisal word by a certain offset: for positive words to decrease, for negative to increase.
Procedures
First weights of all training texts the classified text is calculated. All the texts are placed into a one dimensional emotional space. The proportion of deletions was determined by the cross-validation method
Then the average weights of training texts for each sentiment class were found. The classified text was referred to the class which was located c
Meta
Goals
Result