An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. 1

In TAC 2008 we participated in the main task (Update Summarization) as well as the Sentiment Summarization pilot task. We modified the FastSum system (Schilder and Kondadadi, 2008) and added more aggressive filtering in order to adapt the system to update summarization and sentiment summarization. For the Update Summarization task, we show that a classifier that identifies sentences that are similar to typical first sentences of a news article improves the overall linguistic quality of the generated summaries. For the Sentiment Summarization pilot task, we use a simple sentiment classifier based on a gazetteer of positive and negative sentiment words derived from the General Inquirer and other sources to produce opinion-based summaries for a collection of blog posts given a set of positive and negative questions. 1

Opinion mining techniques add another dimension to search and summarization technology by actually identifying the author’s opinion about a subject, rather than simply identifying the subject itself. Given the dramatic explosion of the blogosphere, both in terms of its data and its participants, it is becoming increasingly important to be able to measure the authority of these participants, especially when professional application areas are involved. After having performed preliminary investigations into sentiment analysis in the legal blogosphere, we are beginning a new direction of work which addresses representing, measuring, and monitoring the degree of authority and thus presumed credibility associated with various types of blog participants. In particular, we explore the utility of authority-detection layered atop opinion mining in the legal and financial domains.

Abstract ― The general perceptions about a product and the reputation of the company determine to a great extent how well the product sells. It is thus imperative that we make efforts to understand the public opinions and sentiments, as they can be a very good indicator of the product’s future sales performance. In this paper, we explore the two most common online media which have been used by the public to express such type of subjective content: Blogs and Micro-blogs. We perform a comparative analysis of the predictive power of the two media to know which of these formats can prove to be a more useful representative of sentiments to an autonomous stock price prediction system.

In recent years, blogs and social networks have particularly boosted interests for opinion mining research. In order to satisfy real-scale applicative needs, a main task is to create or to enhance lexical and semantic resources on evaluative language. Classical resources of the area are mostly built for english, they contain simple opinion word markers and are far to cover the lexical richness of this linguistic phenomenon. We propose a new method, applied on french, to enhance automatically an opinion word lexicon. This learning method relies on linguistic uses of internet users and on semantic tests to infer the degree of subjectivity of many new adjectives, nouns, verbs, noun phrases, verbal phrases which are usually forgotten by other resources. 1.

Abstract. This paper presents an agent-based educational environment to teach argument analysis (ALES). The idea is based on the Argumentation Interchange Format Ontology (AIF) using ”Walton Theory”. ALES uses different mining techniques to manage a highly structured arguments repertoire. This repertoire was designed, developed and implemented by us. Our aim is to extend our previous framework proposed in [3] in order to i) provide a learning environment that guides student during argument learning, ii) aid in improving the student’s argument skills, iii) refine students ’ ability to debate and negotiate using critical thinking. The paper focuses on the environment development specifying the status of each of the constituent modules. 1

Abstract: Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease, making choices with regards to online shopping, choosing events, products, entities. The approaches of text sentiment analysis typically work at a particular level like phrase, sentence or document level. This paper aims at analyzing a solution for the sentiment classification at a fine-grained level, namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral. whereas sentiment analysis attempts to divide the language units into three categories; negative, positive and neutral. With the passage of time and a need for better understanding and extraction, momentum slowly increased towards sentiment classification and semantic orientation. In this paper, various methods proposed for sentence-level sentiment classification have been analyzed. 2. Sentiment Classification Sentiment classification is a new field of Natural Language Processing that classifies subjectivity text into positive or negative.