International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 3, May-June 2015 ISSN: 2347-8578 www.ijcstjournal.org Page 110 A Survey on Sentence Level Sentiment Analysis Vrushali K. Bongirwar [1] Department of Computer Science and Engineering Ramdeobaba College of Engineering & Management Nagpur India ABSTRACT Sentiment Analysis, also called Opinion Mining, is one of the most recent research topics within the field of Information Processing. Textual information retrieval techniques are mainly focused on processing, searching or mining factual information. Textual information also have some objective as well as subjective characteristics. These elements are mainly opinions, sentiments, appraisals, attitudes, and emotions, which are the focus of Sentiment Analysis. Text sentiment analysis typically work at a particular level like phrase, sentence or document level. This paper presents survey of various sentiment analysis methods on different levels. Further it extends the literature on sentence level. Keywords:- Document Level, Phrase Level, Polarity, Sentence Level, Sentiment Analysis. I. INTRODUCTION Sentiment analysis refers to the inference of people’s views, positions and attitudes in their written or spoken texts. Before the coining of the term, the field was studied under names such as subjectivity, point of view and opinion mining. Nowadays, the field is rapidly evolving due to the rise of new platforms such as blogs, social media and user-generated reviews. A large body of work exists on the analysis of latent sentiment in social media platforms such as Twitter. The goal of these studies is to extract timely and relevant information as well as to judge widespread opinions and sentiment. Sentiment Analysis offers many opportunities to develop new applications, especially due to the huge growth of available information in sources such as blogs and social networks. For example, recommendations of items proposed by any recommender system can be computed taking into account aspects such as positive or negative opinions about those items. Review- and opinion-aggregation websites could collect information from different sources in order to summary or compose an opinion about a candidate, product, etc., thus replacing systems which require explicitly opinions or summaries. Therefore one of the most important fields where Sentiment Analysis has a greater impact is in the industrial field. Small and big companies, as well as other organizations such as governments, desire to know what people say about their marques, products or members. II. LEVELS OF ANALYSIS Sentiment analysis has been handled as a Natural Language Processing task at many levels of granularity. Depending on whether the target of study is a whole text or document, one or several linked sentences, or one or several entities or aspects of those entities, different NLP and Sentiment Analysis tasks can be performed. Hence, it is necessary to distinguish three levels of analysis that will clearly determine the different tasks of Sentiment Analysis: (i) document level, (ii) sentence level and (iii) entity/aspect level [1]. A. Document Level Analysis: Document level considers that a document is an opinion on an entity or aspect of it. This level is associated with the task called document-level sentiment classification [10], [11],[12]. The task is to classify whether a whole opinion document expresses a positive or negative sentiment For example, given a product review, the system determines whether the review expresses an overall positive or negative opinion about the product. This task is commonly known as document-level sentiment classification. [19][25]. B. Sentence Level Analysis: The task at this level goes to the sentences and determines whether each sentence expressed a positive, negative, or neutral opinion. Neutral usually means no opinion. This level of analysis is closely related to subjectivity classification which distinguishes sentences (called objective sentences) that express factual information from sentences (called subjective sentences) that express subjective views and opinions. However, we should note that subjectivity is not equivalent to sentiment as many objective sentences can imply opinions. [13], [14]. C. Feature Level Analysis: Both the document level and the sentence level analyses do not discover what exactly people liked and did not like. Aspect level performs finer-grained analysis. Aspect level was earlier called feature level (feature-based opinion mining and summarization).Instead of looking at RESEARCH ARTICLE OPEN ACCESS