Mining opinions from the Web: Beyond relevance retrieval

Abstract

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

Sentiment Analysis by Exploring Large Scale Web-based Chinese Short Text

“…Ku and Chen [12] collected a Chinese sentiment words dictionary called NTUSD, which is widely used nowadays, and determined sentiment polarity of a sentence by identifying sentiment words, negation operators and opinion holders in it. Other research results [13,14] were there based on HowNet, which is another famous Chinese sentiment words dictionary.…”

Modelo de monitoreo de las redes sociales para orientar en la toma de decisiones de las destination management organizations

“…Paine (2011) defiende el monitoreo de los medios sociales como algo continuo y que demanda parámetros de comparación como: diferentes períodos, eventos, sucesos u organizaciones (concurrentes). Al relacionar los conceptos a la actividad turística, es posible pensar en monitorear la retroalimentación de los visitantes sobre sus vacaciones, identificar opiniones positivas y negativas de los consumidores sobre el destino, productos y servicios, identificar fuerzas y debilidades del plan de marketing, ayudar en la elaboración de estrategias de marketing y promoción turística, así como comprender la imagen creada y percibida del destino en la mente de los consumidores (Ku & Chen, 2007;Carson, 2008).…”

CT-SPA: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon

“…Semi-automatic extension methods have been used in building most sentiment dictionaries, such as SentiWordNet (Baccianella, Esuli, & Sebastiani, 2010), SenticNet (Cambria, Speer, Havasi, & Hussain, 2010), WordNet-Affect (Strapparava, & Valitutti, 2004), and Chinese NTUSD (Ku & Chen, 2007). The most typical process is the procedure proposed by Whitelaw, Garg, & Argamon (2005).…”