User-generated content (UGC) based on customer opinions and experiences has increasingly become a rich resource of business opportunities. This study proposes a change mining framework to address the key shortcomings of the traditional SWOT analysis, including subjectivity in defining factors, lack of empirical validation, inflexible to meet the changing environment, and lack of actionable steps. Product reviews were collected for several brands from Amazon.com. Consumer sentiment was calculated to build an aspect-based opinion database, followed by the application of change mining to the opinion database for different generations of products. Factors were then extracted for the four key categories of SWOT. Our approach not only answers the four key shortcomings of traditional SWOT analysis, but also offers additional opportunities, such as the ability to monitor trends, and flexibility to frame SWOT factors based on desired time periods. Further managerial insights are provided.

User-Defined SWOT analysis – A change mining perspective on user-generated content

In the second part of the article the methods for main subtasks of aspectbased sentiment analysis are described. The method for explicit aspect term extraction relies on the vector space of distributed representations of words. The term polarity detection method is based on use of pointwise mutual information and semantic similarity measure. Results from SentiRuEval workshop for automobiles and restaurants domains are given. Proposed methods achieved good results in several key subtasks. In aspect term polarity detection task and sentiment analysis of whole review on aspect categories methods showed the best result for both domains. In the aspect term categorization task our method was placed at the second position. And for explicit aspect term extraction the first result obtained for the restaurant domain according to partial match evaluation criteria.

Semantic similarity for aspect-based sentiment analysis.

Aspect category detection using statistical and semantic association

Online learning has developed rapidly and billions of learners have participated in various courses. However, the high dropout rate is universal and learning performance is not satisfactory. Fortunately, learners have posted a large number of reviews which express their feedback opinions. The fine-grained aspects and opinions existing in reviews provide valuable information for educators to improve learning performance. Thus, it is necessary to make fine-grained aspect-based opinion mining on course reviews for feedback analysis. First, the latent Dirichlet allocation is applied to generate topics and the topic related words from course reviews. Each word belongs to a topic and these words are considered as the core aspects. Second, a series of rules and an algorithm are designed based on dependency syntax analysis to extract the fine-grained aspects and opinions. The aspect-based opinion candidate set is generated. Then the corresponding opinions of the core aspects are selected from the candidate set. Third, the sentiment score of each opinion is calculated combining the dictionary-based and pointwise mutual information method to identify the polarity of the opinion. The extracted aspects and opinions with sentiment polarity provide fine-grained feedback information from several topic perspectives. 

Fine-grained aspect-based opinion mining on online course reviews for feedback analysis

This work was supported by the Spanish CIEN project LPS-BIGGER cofunded by the MINECO and CDTI (IDI-20141260) and TUNER project TIN2015-65308-C5-5-R (MINECO/FEDER, UE)

/pdf/classifying-short-texts-for-a-social-media-monitoring-system-2far71o9p0.pdf

Classifying short texts for a Social Media monitoring system

This paper describes our approach for the fourth task of the SemEval 2014 challenge: Aspect Based Sentiment Analysis. Our system is designed to solve all four subtasks: (i) identifying aspect terms, (ii) determining the polarity of an aspect term, (iii) detecting aspect categories, and (iv) determining the polarity of a predefined aspect category. Our system is based on the Stanford sentiment tree.

/pdf/itac-aspect-based-sentiment-analysis-using-sentiment-trees-23lwkixcoj.pdf

iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries

Sentiment Analysis (engl. f¨ ur Stimmungsanalyse) wird verwendet, um Mei- nungen, Gef¨ uhle und Emotionen aus Texten zu extrahieren. In diesem Artikel wird ein Ansatz zur Sentiment Analysis auf Basis von Aspekten beschrieben. Dieser An- satz kann unter anderem f¨ ur die automatisierte Klassifizierung von Produktkommen- taren, wie sie beispielsweise auf Bewertungsplattformen vorkommen, verwendet wer- den. Dieser Ansatz gliedert sich in vier wesentliche Teilaufgaben: (i) Extraktion der Aspekte, (ii) Bewertung der Polarit¨ at der Aspekte, (iii) Klassifizierung der Aspekte in vorgegebene Kategorien und (iv) Bewertung der Polaritder Kategorie. Zur Umset- zung des Ansatzes werden Methoden der maschinellen Sprachverarbeitung eingesetzt.

/pdf/aspekt-basierte-sentiment-analysis-14rjgh2x5g.pdf

Aspekt-basierte Sentiment Analysis

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iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries

Aspekt-basierte Sentiment Analysis