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.

Web 2.0 technology has enabled more and more people to freely express their opinions on the Web, making the Web an extremely valuable source for mining user opinions about all kinds of topics. In this paper we study how to automatically integrate opinions expressed in a well-written expert review with lots of opinions scattering in various sources such as blogspaces and forums. We formally define this new integration problem and propose to use semi-supervised topic models to solve the problem in a principled way. Experiments on integrating opinions about two quite different topics (a product and a political figure) show that the proposed method is effective for both topics and can generate useful aligned integrated opinion summaries. The proposed method is quite general. It can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.

We demonstrate the next release of MoodViews, a set online tools mood analysis in blogs. Since its initial launch in mid-2005, MoodViews has provided a window into aggregate states of mind of masses of people. In addition to the tracking functionalities that MoodViews has offered so far, we demonstrate several types of mood-related search tools. These include searching for moods most closely associated with a given topic, and ranking blog posts not just by publication date or relevancy for a topic (as most blog search engines do), but also by mood (e.g., the “happiest ” post on a given topic). 1.

Future machines will connect with users on an emotional level in addition to performing complex computations (Norman 2004). In this article, we present a system that adds an emotional dimension to an activity that Internet users engage in frequently, search. ESSE, which stands for Emotional State Search Engine, is a web search engine that goes beyond facilitating a user’s exploration of the web by topic, as search engines such as Google or Yahoo! afford. Rather, it enables the user to browse their topically relevant search results by mood, providing the user with a unique perspective on the topic at hand. Consider a user wishing to read opinions about the new president of the United States. Typing “President Obama ” into a Google search box will return (among other results), a few recent news stories about Obama, the Whitehouse’s website, as well as a wikipedia article about him. Typing “President Obama ” into a Google Blog Search box will bring the user a bit closer to their goal in that all of the results are indeed blogs (typically opinions) about Obama. However, where blog search engines fall short is in providing users with a way to navigate and digest the vastness of the blogosphere, the incredible number of results for the query “President Obama ” (approximately 17,335,307 as of 2/24/09) (Google Blog Search 2009). ESSE provides another dimension by which users can take in the vastness of the web or the blogosphere. This article outlines the contributions of ESSE including a new approach to mood classification.

The present discussion highlights the aspects of an ongoing doctoral thesis grounded on the analysis and tracking of emotions from English and Bengali texts. Development of lexical resources and corpora meets the preliminary urgencies. The research spectrum aims to identify the evaluative emotional expressions at word, phrase, sentence, and document level granularities along with their associated holders and topics. Tracking of emotions based on topic or event was carried out by employing sense based affect scoring techniques. The labeled emotion corpora are being prepared from unlabeled examples to cope with the scarcity of emotional resources, especially for the resource constraint language like Bengali. Different unsupervised, supervised and semi-supervised strategies, adopted for coloring each outline of the research spectrum produce satisfactory outcomes.

Travel websites like TripAdvisor are nowadays important tools for travelers when deciding which hotels to stay in, and what restaurants and tourist attractions to visit. In this paper, we study opinion mining applied on data from travel review sites. We also describe how the results of sentiment analysis of textual reviews can be visualized using Google Maps, providing possibilities for users to easily detect good hotels and good areas to stay in. More advanced features also provides for faceted and filtered visualization. An evaluation of the techniques presented, shows high accuracy in opinion mining, and that the prototype can help detect hotel features and possible reasons for changes in opinion as well as show "good " and "bad " geographical areas based on hotel reviews.