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Opinion mining (also called sentiment analysis) is a type of natural language processing for computing people's opinions and emotions. It detects opinions from structured, semi-structured, and unstructured social media contents at different levels, such as the document, word, sentence, and aspect levels. In all these levels except aspect, opinion mining identifies the overall subjectivity or sentiment polarities. An aspect level is described as a part or an attribute of an entity. It exactly describes people's likes and dislikes in social media contents. In this paper, we propose a new framework for ranking products based on aspects. First, the system identifies the aspects of products. Second, the aspects and their opinion words are identified and visualized from the products’ reviews using a Harel–Koren fast multiscale layout. Third, the network visualization is constructed and modeled, and a Spearman's rank correlation coefficient based opinion ranking method is applied to rank the products based on positive and negative ranks. Fourth, the supervised learning methods (Naïve Bayes, Maximum Entropy, and Support Vector Machine) are employed for the aspect-based sentiment classification task. Finally, the performance of the system is measured by the experimental results. [ABSTRACT FROM AUTHOR]

Social networks messaging typically contains a lot of implicit linguistic information partially due to restrictions on a message’s length (i.e., few named entities, short sentences, no discourse structure, etc.). This may significantly impact several applications including opinion mining, sentiment analysis, etc., as data collection tasks such as opinion retrieval tasks will fail to obtain all the relevant messages whenever the target topic, objects, or features are not explicit within the texts. In order to address these issues, in this paper a novel adaptive approach for opinion retrieval is proposed. It combines natural-language co-referencing techniques, features-based linguistic preprocessing and memory-based learning to resolving implicit co-referencing within informal opinion texts by using underlying hierarchies of thread messages. Experiments were conducted to assess the ability of the model to improve opinion retrieval by resolving implicit entities and features, showing the promise of our opinion retrieval approach when compared to state-of-the-art methods using text data from social networks. [ABSTRACT FROM AUTHOR]