Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

Modèles de langues pour la détection d'opinions dans les blogs

Cet article décrit une approche de recherche de documents pertinents vis-à-vis d’une requête et exprimant une opinion. Afin de détecter si un document est porteur d’opinion (i.e. comporte de l’information subjective), nous proposons de le comparer à des sources d’information qui comportent du contenu de type opinion. L’intuition derrière cela est la suivante : un document ayant une similarité forte avec des sources d’opinions, est vraisemblablement porteur d’opinion. Pour mesurer cette similarité, nous exploitons des modèles de langue. Nous modélisons le document et la source (référence) porteuse d’opinions par des modèles de langue, nous évaluons ensuite la similarité de ces modèles. Plusieurs expérimentations ont été réalisées sur des collections issues de TREC. Les résultats obtenus valident notre intuition

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Distinguishing the Popularity Between Topics: A System for Up-to-date Opinion Retrieval and Mining in the Web

The constantly increasing amount of opinionated texts found in the Web had a significant impact in the development of sentiment analysis. So far, the majority of the comparative studies in this field focus on analyzing fixed (offline) collections from certain domains, genres, or topics. In this paper, we present an online system for opinion mining and retrieval that is able to discover up-to-date web pages on given topics using focused crawling agents, extract opinionated textual parts from web pages, and estimate their polarity using opinion mining agents. The evaluation of the system on real-world case studies, demonstrates that is appropriate for opinion comparison between topics, since it provides useful indications on the popularity based on a relatively small amount of web pages. Moreover, it can produce genre-aware results of opinion retrieval, a valuable option for decision-makers

Finding Thoughtful Comments from Social Media

<p>Performance, power and state transition in one day at R = 5.</p

A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user’s query. One of the key issues is how to combine a document’s opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1 % and 40.3 % have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario

Taming the hashtag: universal sentiment, SPEQ-ing the truth, and structured opinion in social media

Opinions are valuable, and with the advent of social media, plentiful. Opinions are not always intelligible, however. Therefore, many of the views of social media users are ignored. This dissertation seeks to confront the challenges associated with opinion mining and sentiment analysis by investigating three aspects of opinion expression and consumption in social media. The universality of opinion itself is explored through an innovative application of social science research in survey construction, semantic distance analysis, and corpus linguistics. Results include a universal taxonomy of 18 sentiment types shown to be portable across 15 languages. The universality of opinion processing is explored through a qualitative meta-synthesis (QMS) analysis of social psychology, opinion mining and sentiment analysis, and voting systems scholarship. Results include a comprehensive theoretical model of opinion processing: the States, Processes, Effects, and Quality (SPEQ) model for opinion mining and sentiment analysis. SPEQ defines seven states of opinion, six processes which govern the transitions between those states and five quality and integrity measures for the evaluation of those processes. Lastly, the concept of a structured opinion syntax is explored. Despite strong resentment to symbolic representations of meaning by subjects, learning and priming effects for both the encoding and decoding of structured opinion support the contention that such a syntax could be developed and used. Many future directions for research are presented for each aspect of opinion investigated

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data