New Avenues in Opinion Mining and Sentiment Analysis

By Erik Cambria, Björn Schuller, Yunqing Xia, Catherine Havasi. IEEE Intelligent Systems, 2013

1,274 citations recorded by the source corpus.

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/fc9b02a1c48f7f0578cc45cdd8a00ae91854aba4

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vadlamani Ravi
2. Flavius Frasincar
3. Kim Schouten
4. Maja Pantic
5. Lidong Bing
6. Kumar Ravi
7. Zongben Xu
8. Jun Shu

Citations in the corpus

• The evolution of sentiment analysis - A review of research topics, venues, and top cited papers (2016, background): “The top-cited papers for 2012 and 2013 were both among the top-five cited all time and were already summarized [Cambria et al. 2013; Liu and Zhang 2012].”
• Opinion Mining and Information Fusion: A survey (2016, background): “…the task of identifying positive and negative opinions, emotions and evaluations " [16], " the task of finding the opinions of authors about specific entities " [5], " tracking the mood of the public about a particular product or topic " [17], or simply " the task of polarity classification " [18].”
• Survey on Aspect-Level Sentiment Analysis (2016, methodology): “When considering the future of aspect-level sentiment analysis, we foresee a move from traditional word-based approaches, towards semantically rich concept-centric aspect-level sentiment analysis [95].”
• A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “Cambria et al. (2013) has also presented the concept level of opinion mining as a newavenue in the sentiment analysis.”
• A survey of multimodal sentiment analysis (2017, background): “[6] suggested that opinion mining and sentiment analysis focus on two different problems of polarity detection and emotion recognition.”
• Text mining for market prediction: A systematic review (2014, background): “There is a body of research that is focused on sentiment analysis or the so called ''opinion mining'' (Balahur, Steinberger, Goot, Pouliquen, & Kabadjov, 2009; Cambria, Schuller, Yunqing, & Havasi, 2013; Hsinchun & Zimbra, 2010).”
• Sentiment analysis: A review and comparative analysis of web services (2015, positive): “According to Cambria, Sentiment Analysis can be considered as a very restricted NLP problem, where it is only necessary to understand the positive or negative sentiments concerning each sentence and/or the target entities or topics [12].”
• News impact on stock price return via sentiment analysis (2014, background): “Concept-level approach on sentiment mining has been a trend recently [7,18,19].”
• SemEval-2016 Task 5: Aspect Based Sentiment Analysis (2016, background): “…every aspect (e.g., entity recognition, coreference resolution, negation handling) of Natural Language Processing (Liu, 2012) and as Cambria et al. (2013) mention “ it requires a deep understanding of the explicit and implicit, regular and irregular, and syntactic and semantic language rules ”.”
• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “[15] pointed out complexities involved in SA with respect to current demand along with possible future research directions.”
• Sentiment analysis algorithms and applications: A survey (2014, background): “Cambria and Schuller et al. [9], Feldman [10] and Montoyo and Martı´nez-Barco [11] have given short surveys illustrating the new trends in SA.”
• Small Sample Learning in Big Data Era (2018, background): “, 2013), sentiment analysis (Cambria et al., 2013), machine translate (Sutskever et al.”
• Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, background): “The motivation behind this work is twofold: First, although market-prediction through text-mining is shown to be a promising area of work in the literature, the text-mining approaches utilized in it at this stage are not much beyond basic ones as it is still an emerging field.”