Ocular Systems Pune Info@ocularsystems.inOpinion Mining & Sentiment Analysis Based on Natural Language Processing

AbstractThe main goal of social network analysis is the study of structural properties of networks. Structural analysis of the social network investigates the properties of individual vertices and the global properties of the network as a whole. It answers two basic classes of questions about the network: what is the structural position of any given individual node and what can be said about groups forming within the network. The main measurement of a nodes social power is centrality, which allows to determine nodes relative and absolute importance in the network. There are several methods to determine nodes centrality, such as the degree centrality the betweenness centrality or the closeness centrality. The proposed system will able to mine users intent from comments. Irrelevant comments removal will increase opinion mining performance of system. False positive & false negative rates may reduced. Resistant to fake opinion postings. Motivation of the Project- To overcome the fake posting problems in web application.- It can be used in any of the social website to find positive and negative comments over the products.- It will reduce the percentage of fake postings.

System Architecture Diagram

Modules in the project To develop website to post advertise & accept comments Offline comment data collection POS Feature Extraction NER Features Extraction N-Gram Extraction Train Classifier Comment-Advertise clarity score Irrelevant Ad removal Classification in 3 classes: Positive, Negative, Neutral Graphical result plotting

POS & NER Tagger POS Tagger: POS-tags can be used in extraction of words of a specific word class (all finite verbs, all nouns, etc.), to decide which word class a word belongs to in a given position (She flies = verb, the flies = noun), or to group word classes into syntagmata.

Used as important feature set in NLP. Built by Stanford university

N-Gram Extraction N-Gram is noting but the collection of keywords appeared in a statement. For Example: One Gram: prime Bi-Gram: Prime Minister Tri-Gram: Prime Minister India Along with N-Grams their frequency counts are used as feature set to NLP Classifier.Porter Stemmer Algorithm Used for suffix removal. Plays important role in frequency count mining. Example: Suppose we want to extract frequency count of play in paragraph. Then we simply use string comparison. It would be problematic if paragraph contains plays, played, playing, etc. From keywords like Red, ed should not be removed. For this reason porter has designed algorithm for stemming.

Stop Word Removal All stop words, for example, common words, such as a and the, are removed from multiple word queries to increase performance of data mining systems. Steps: Create database of stopwords Remove each occurance of stop word which is present in our database.Nave Bayes classifier Nave Bayes algorithm is probabilistic algorithm Algorithm takes input in ARFF (Attribute relationship file format) & gives probability for each class. If comment says:I purchased this product, it seems fake, performance of the gadget is poor Classifier would suggest probabilities: Positive: 2%, Negative 78%, Neutral: 20% Though algorithm is based on probabilities for prediction, called as NP-Hard problem. So, we will choose highest probability results to calculate summary.

Software requirementOperating System: Windows Technology: Java and J2EEWeb Technologies: Html, JavaScript, CSS IDE : Netbeans 7.4 Web Server: Apache Tomcat 6.0 Database: My SQL Java Version : J2SDK1.6/7 Conclusion The proposed system will able to mine users intent from comments. Irrelevant comments removal will increase opinion mining performance of system. False positive & false negative rates may reduced. Resistant to fake opinion postings.

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