Download A Survey on Sentiment Analysis and Opinion Mining

International Conference on Computing and
Mathematical Sciences – ICCMS’2017
February 25-26, 2017
Invent, Innovate and Integrate for Socioeconomic Development
A Survey on Sentiment Analysis and Opinion Mining
Iqra Kanwal1, Iqra Amjad2, Muhammad Sajid2 , Muhammad Shakeel Asghar2
Department of CS & IT University of Lahore, Gujrat Campus, Gujrat, Pakistan
2
Department of CS & IT University of Lahore, Gujrat Campus, Gujrat, Pakistan
1
[email protected], 2 [email protected], 2 [email protected],
2
[email protected]
1
Abstract—Now a day’s people get and share information through reviews or opinions via
internet. Customer reviews containing very valuable information about different products
and topics. There are different sources of data on web like social websites, discussion
forums, and blogs containing such opinions. These sources mostly contain unstructured data
need to be analyzed. This issue draws our attention to study the field of Opinion Mining and
Sentiment Analysis. Opinion mining is basically Natural Language Processing and
Information Extraction task which mine useful information and classify user reviews.
Sentiment analysis is to classify reviews and assign polarity as positive, negative or neutral.
In this paper we discuss different methods and techniques to classify reviews by using
Machine Learning Techniques named as Supervised Learning techniques and Unsupervised
Learning Techniques. In Supervised Machine Learning technique training data set is used
classify sentence or document into finite set of classes, so we already know input and
expected output. In Supervised Machine Learning Naïve-Bayes and Support-Vector
Machine (SVM) K-nearest neighbor (KNN) is used at Sentence Level Sentiment
Classification. . Maximum Entropy Classifier (ME) finds probabilistic model to satisfy any
constraint, it converts label features into vectors by using encoding. In Unsupervised
Machine Learning clustering algorithms are used for classification purpose training data set
not used. Algorithms include K-means clustering and Hierarchical clustering. Different
tools for sentiment analysis and opinion mining are NTLK, GATE, OPEN NLP, LING
PIPE, and OPINION FINDER. Product/Service purchasing, marketing research,
Recommendation system, spam Opinion Detection etc. are the application areas of
sentiment analysis and opinion mining.
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