IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. I (Nov – Dec. 2015), PP 157-162 www.iosrjournals.org DOI: 10.9790/0661-1761157162 www.iosrjournals.org 157 | Page Sentiment of Sentence in Tweets: A Review Sandip Mali 1 , Ashish Balerao 1 , Suvarnsing Bhable 1 , Sangramsing Kayte 1, 1, Department of Computer Science and Information Technology Dr. Babasaheb Ambedkar Marathwada University, Aurangabad Abstract: Determine the sentiment of sentence that is positive or negative based on the presence of part of speech tag, the emoticons present in the sentences. For this research we use the most popular microblogging sit twitter for sentiment orientation. In this paper we want to extract tweets form the twitter related to the product like mobile phones, home appliances, vehicle etc. After retrieving tweets we perform some preprocessing on it like remove retweets, remove tweets containing few words with minimum threshold of length five, remove tweets containing only urls. After this the remaining tweets are pre-processed like that transform all letters of the tweets to the lower case then remove punctuation from the tweets because it reduces the accuracy of result. After this remove extra white spaces from the tweets, then we apply a pos tagger to tag each word. The tuple after the applying above steps contain (word, pos tag, English-word, stop-word). We are interested in only tweets that contain opinion and eliminate the remaining non-opinion tweets from the data set. For this we use the Naïve Bays classification algorithm. After this we use short text classification on tweets i.e., the word having different meaning in different domain. In order to solve this problem we use two different feature selection algorithms the mutual information (MI) and the X2 feature selection. At final stage predicting the orientation of an opinion sentence that is positive or negative as we mentioned above. For this we use two model like unigram model and opinion miner. Keywords: Compositional Semantic Rule Algorithm, Numeric Sentiment Identification Algorithm, Bag-of-Word and Rule-based Algorithm, CRF Tagger, POS tagger I. Introduction Twitter is a popular real-time microblogging service that allows its users to share short pieces of information known as ―tweets‖, means tweet is the small text that would be generated by user related to certain things like product, his own opinion, his beliefs etc. The only problem with tweet is that its length should be less than 140 characters. First we will introduce various properties of messages that users post on Twitter. Some of the many unique properties include the following: a) Usernames: Users often include Twitter usernames in their tweets in order to direct their messages. A de facto standard is to include the @ symbol before the username (e.g @liang). b) Hash Tags: Twitter allows users to tag their tweets with the help of a ―hash tag‖, which has the form of #<tagname>‖. Users can use this to convey what their tweet is primarily about by using keywords that best represent the content of the tweet. c) RT: If a tweet is compelling and interesting enough, users might republish that tweet, commonly known as retweeting, and twitter employs ―RT‖ to represent re-tweeting (e.g. ―RT @RodyRoderos: I love iphone 6 but i want Samsung note 2 :(‖). Tweets are also called as the microblog because of its short text. Microblogging websites have evolved to become a source of varied kind of information. This is due to nature of microblogs on which people post real time messages about their opinions on a variety of topics, discuss current issues, complain, and express their opinion for products they use in daily life. Due to this, Microblogging websites have evolved to become a source of a diverse variety information, with millions of messages appearing daily on popular web-sites. Product reviewing has been rapidly growing in recent years because more and more products are selling on the Web. The large number of reviews allows customers to make informed decisions on product purchases. However, it is difficult for product manufacturers or businesses to keep track of customer opinions and sentiments on their products and services. In order to enhance the customer shopping experiences a system is needed to help people analyze the sentiment content of product reviews. A) Why opinions are important? Opinions are central to almost all human activities because they are key influencers of our behaviors. Whenever we need to make a decision, we want to kn ow others’ opinions. In the real world, businesses and organizations always want to find consumer or public opinions about their products and services. Individual consumers also want to know the opinions of existing users of a product before purchasing it, and others’ opinions about political candidates before making a voting decision in a political election. In the past, when an