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 material on 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.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.

/pdf/earthquake-shakes-twitter-users-real-time-event-detection-by-1tue7cvj9p.pdf

Earthquake shakes Twitter users: real-time event detection by social sensors

In the recent years, micro blogging platforms like Twitter have become instrumental in gauging public mood. So it makes it a feasible predictive strategy to speculate the rise and fall of stock prices. This paper aims to undertake a stepwise methodology to determine the effects of an average person's tweets over fluctuation of stock prices of a multinational firm called Samsung Electronics Ltd. It involves extracting tweets from twitter, data cleansing and application of a suitable algorithm in order to get the adequate sentiment analysis. The vast impact created by twitter data feed has been greatly studied in this paper. Attempts have been made to design an algorithm which works well analysing the positive, negative and neutral tweets.

Opinion mining and sentiment analysis

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The results highlight the potential of text streams as a substitute and supplement for traditional polling.

/pdf/from-tweets-to-polls-linking-text-sentiment-to-public-1v3t43hoey.pdf

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.

/pdf/topic-sentiment-mixture-modeling-facets-and-opinions-in-54w990mu8i.pdf

Topic sentiment mixture: modeling facets and opinions in weblogs

Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. Evolution of patterns can be discovered by comparative analysis of theme life cycles and theme snapshots. Experiments on three different data sets show that the proposed approach can discover interesting spatiotemporal theme patterns effectively. The proposed probabilistic model is general and can be used for spatiotemporal text mining on any domain with time and location information.

/pdf/a-probabilistic-approach-to-spatiotemporal-theme-pattern-5b05x658m5.pdf

A probabilistic approach to spatiotemporal theme pattern mining on weblogs

This paper presents the duality hypothesis of search and tagging, two important behaviors of web users. The hypothesis states that if a user views a document D in the search results for query Q, the user would tend to assign document D a tag identical to or similar to Q; similarly, if a user tags a document D with a tag T , the user would tend to view document D if it is in the search results obtained using T as a query. We formalize this hypothesis with a unified probabilistic model for search and tagging, and show that empirical results of several tasks on search log and tag data sets, including ad hoc search, query suggestion, and query trend analysis, all support this duality hypothesis. Since the availability of search log is limited due to the privacy concern, our study opens up a highly promising direction of using tag data to approximate or supplement search log data for studying user behavior and improving search engine accuracy.

/pdf/searching-and-tagging-two-sides-of-the-same-coin-4dvs4mi5yj.pdf

Searching and Tagging: Two Sides of the Same Coin?

This paper introduces a new approach of webpage template extraction. Unlike traditional methods which concern only content information, this paper considers both structure and content similarity. It uses natural table structure as content units instead of text blocks or pagelets. This paper novelly and formally defines the templates and other concepts. It introduces a new concept, candidate template, which is an intermediate level of abstract table structure. A candidate template only covers the most informative tables, and abstracts a large page set with similar structures. This paper proposes a novel approach of template extraction by solving three sub problems surrounding candidate template set. The involving of candidate template set solves the accuracy and efficiency problems of traditional approaches. This paper also introduces a new model for structural similarity, and for table informativeness based on six heuristics.

Template extraction from candidate template set generation: a structure and content approach

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Papers

Topic sentiment mixture: modeling facets and opinions in weblogs

A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Searching and Tagging: Two Sides of the Same Coin?

Template extraction from candidate template set generation: a structure and content approach