USER-GENERATED-CONTENT (UGC) IN THE FORM OF ONLINE REVIEWS CAN BE AN INVALUABLE SOURCE OF INFORMATION FOR BOTH CUSTOMERS AND BUSINESSES. SENTIMENT ANALYSIS AND OPINION MINING TOOLS AND TECHNIQUES HAVE BEEN PROPOSED IN THE LITERATURE TO EXTRACT KNOWLEDGE FROM ONLINE REVIEWS. ASPECT-BASED OPINION MINING WHICH HAS GAINED GROWING ATTENTION MAINLY HAS TWO TASKS INCLUDING ASPECT EXTRACTION AND SENTIMENT POLARITY DETECTION. ONCE AN ASPECT-BASED OPINION MINING TASK HAS BEEN ACCOMPLISHED; A BAG OF SENTIMENTS WILL BE ACHIEVED. IN MANY CASES, IT IS NECESSARY TO OBTAIN AN OVERALL SENTIMENT ABOUT A TYPICAL ASPECT. IN THIS STUDY, WE HAVE PROPOSED A SENTIMENT AGGREGATION SYSTEM BASED ON WEIGHTED SELECTIVE AGGREGATED MAJORITY OWA (WSAM-OWA). WSAM-OWA CONSIDERS BOTH THE MAJORITY AND THE DEGREE OF IMPORTANCE OF INFORMATION SOURCE IN THE PROCESS OF AGGREGATION. THE PROPOSED SYSTEM EXPLOITS THE HELPFULNESS RATING OF REVIEWS IN DETERMINING THE RELIABILITY AND CREDIBILITY OF EACH SENTIMENT. A CASE STUDY WAS CONDUCTED TO ILLUSTRATES THE USEFULNESS OF THE PROPOSED SYSTEM. THE RESULTS OF THIS STUDY DEMONSTRATED THAT THE PROPOSED SENTIMENT AGGREGATION SYSTEM COULD BE INCORPORATED IN OPINION MINING SYSTEMS.

WITH THE RAPID GROWTH OF THE WEB AND ITS USERS AROUND THE WORLD, THE AVAILABILITY AND USEFULNESS OF USERS’ COMMENTS HAVE INCREASED IN RECENT YEARS. SENTIMENT ANALYSIS TECHNIQUES ARE USED TO EXTRACT VALUABLE KNOWLEDGE FROM THIS INCREASING VOLUME OF TEXTUAL INFORMATION. THESE TECHNIQUES USUALLY DECOMPOSE EVERY COMMENT TO ITS SENTENCES, DETECT THEIR SENTIMENTS, AND AGGREGATE THESE SENTIMENTS TO CALCULATE THE OVERALL SENTIMENT BEING CONVEYED IN THE COMMENT. IN THIS STUDY, A NEW SENTENCE-LEVEL AGGREGATION MECHANISM BASED ON UNINORM OPERATORS IS PROPOSED FOR AGGREGATING SENTENCE-LEVEL SENTIMENT INTO AN OVERALL DOCUMENT-LEVEL OPINION. IN ORDER TO SHOW THE UTILITY OF THE PROPOSED METHOD, IT IS APPLIED TO POLARITY DETECTION AND SCORE PREDICTION PROBLEMS ON FOUR LARGE PERSIAN REVIEW DATASETS. IMPLEMENTATION RESULTS SHOW THAT THE PROPOSED METHOD, IN COMPARISON WITH DEMPSTER-SHAFER AGGREGATION METHOD, ACHIEVES A HIGHER PERFORMANCE IN POLARITY DETECTION WHILE THE DEMPSTER-SHAFER METHOD SLIGHTLY OUTPERFORMS THE PROPOSED METHOD IN SCORE PREDICTION TASK.

SENTIMENT ANALYSIS IS AN AREA OF STUDY WITHIN NATURAL LANGUAGE PROCESSING THAT IS CONCERNED WITH IDENTIFYING THE MOOD OR OPINION OF SUBJECTIVE ELEMENTS WITHIN A TEXT. THIS PAPER FOCUSES ON THE VARIOUS METHODS USED FOR CLASSIFYING A GIVEN PIECE OF NATURAL LANGUAGE TEXT ACCORDING TO THE OPINIONS EXPRESSED IN IT I.E. WHETHER THE GENERAL ATTITUDE IS NEGATIVE OR POSITIVE. WE ALSO DISCUSS THE TWO-STEP METHOD (ASPECT CLASSIFICATION FOLLOWED BY POLARITY CLASSIFICATION) THAT WE FOLLOWED ALONG WITH THE EXPERIMENTAL SETUP.

SOCIAL NETWORKS ARE THE MAIN SOURCE OF USER OPINIONS ABOUT EVENT AND PRODUCT. EXTRACTING USER SENTIMENT FROM THEIR COMMENTS IN SOCIAL NETWORKS VERY HELPFUL FOR COMPANIES AND GOVERNMENTS FOR THEIR DEVELOPMENT PLAN. TWITTER CONSISTS OF BILLIONS OF USER AND THEIR OPINIONS AND IT IS A GOOD SOURCE FOR SENTIMENT ANALYSIS. LOTS OF WORKS PROPOSED IN RECENT YEARS ABOUT SENTIMENT ANALYSIS IN TWITTER. VARIOUS METHODS ARE USED TO DEVELOP A SA METHOD SUCH AS NLP BASED, MACHINE LEARNING BASED AND HYBRID METHODS. BUT, ALL OF THESE METHODS DON’T SATISFY ALL REQUIREMENTS OF THIS RESEARCH AREA. IN THIS REPORT WE TRY TO REVIEW THE IMPORTANT SOLUTIONS ARE PROPOSED FOR THIS PROBLEM.THIS PAPER CONSISTS OF FIVE CATEGORIES: 1) TO INTRODUCE AND TO ORIENTATE WITH THE FIELD OF SENTIMENT ANALYSIS IN TWITTER SOCIAL NETWORKS 2) TO REVIEW THE WORKS DONE IN THE AREA OF SA 3) TO INTRODUCE CONFERENCES RELATED TO THE FIELD OF SA IN RECENT YEARS THAT HOLD COMPETITIONS WITH THEIR RESULTS AND THEIR BEST PRACTICES OFFERED 4) TO INTRODUCE AVAILABLE DATASETS 5) TO INTRODUCE A FEW AVAILABLE MASH-UPS.