aspect based opinion mining

Aspect Based Opinion Mining
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Aspect Based Opinion Mining
A majority of the consumers rely on user information posted on social media sites and blogs. Particularly, the business world has immensely benefitted from the growth of social media platforms. Today’s entrepreneurs do not blindly enter into business without conducting researches regarding the perception of the consumer on a particular product or service in the market. Social media platforms have emerged to be ideal platforms upon which customer reviews are posted. The data collected is instrumental in providing business owners with relevant avenues upon which they can enhance their products or services (Bauman, Liu & Tuzhilin, 2016). The reviews are also instrumental in informing entrepreneurs of the right decisions to undertake that would enable them respond to the concerns of the consumers. However, the processes involved in gathering of customer opinions tends to be a complicated process (Santosh, Babu, Prasad & Vivekananda, 2016). It is imperative that the information gathered be meaningful and helpful to the business owner. Opinion based mining has been identified as one of the most ideal methodologies to gather customer reviews and opinions.
Aspect-Based Opinion Mining (ABOM) has been identified as the most ideal methodology of onion mining to gather data. The method examines every feature of a review including the feature levels of a sentence. ABOM ensures that it analyzes all the multi-expressions of a code and its relationships with specific word constructs to give the best combinations of what a review entails.

Valencia-García, Colomo-Palacios and Alor-Hernández (2016) cite that the major advantage of the ABOM method is that it identifies the best combination of features and hence the most eligible aspect of a word as a product. The way it works is different from other methods, which only consider single feature entities when analyzing reviews. Thus, ABOM methods involve the extraction of all possible aspects and opinions concerning a product irrespective of the linguistic features in use.
Previous studies on the use of ABOM indicate that the feature is highly efficient and is able to classify content at word and even phrase level. ABOM was first conceived from the concept of machine learning techniques that mostly analyzed movies. To this effect, they could select criticisms of movie reviews and analyze whether it was able to capture the most intricate details of the review. Further research identified that the use of information extraction techniques based on frequencies had higher degrees of precisions (Wang, Chen & Liu, 2016). The study by Santosh et al. (2016), identified that the development of a system that could filter frequent nouns would be useful in analyzing contents depending on the frequency that a phrase appears. The ability of the technique to filter specific words was identified to be 54% efficient (Federici & Dragoni, 2016,). However, the model was touted to be inefficient because it was discriminative and could not independently generate words f there were no overlapping features to facilitate the extraction.
The CRF (Conditional Random Fields) model was designed to use nouns and noun phrases as the most ideal ways to filter words concerning a review. However, the model emphasizes on the need of words to make a meaningful sequence depending on set grammatical rules and the aspects of natural languages. The sequence was found to outdo other algorithms because it was able to compute the most probable outcomes of a word. The use of CRF was found to give a viable alternative with enhanced accuracy levels. To this effect, end users can benefit from useful reviews and opinions regarding products in the shortest time possible. Its ability to match basic linguistic features and statistical aspects plays an influential role in ensuring the attainment of a high performance when extracting information regarding reviews.
References
Bauman, K., Liu, B., & Tuzhilin, A. (2016). Recommending Items with Conditions Enhancing User Experiences Based on Sentiment Analysis of Reviews. CBRecSys 2016, 19.
Federici, M., & Dragoni, M. (2016, May). A knowledge-based approach for aspect-based opinion mining. In Semantic Web Evaluation Challenge (pp. 141-152). Springer International Publishing.
Samha, A. K., Li, Y., & Zhang, J. (2015, August). Aspect-based opinion mining from product reviews using conditional random fields. In Data Mining and Analytics: Proceedings of the 13th Australasian Data Mining Conference [Conferences in Research and Practice in Information Technology, Volume 168] (pp. 119-128). Australian Computer Society.
Santosh, D. T., Babu, K. S., Prasad, S. D. V., & Vivekananda, A. (2016). Opinion Mining of Online Product Reviews from Traditional LDA Topic Clusters using Feature Ontology Tree and Sentiwordnet.
Valencia-García, R., Colomo-Palacios, R., & Alor-Hernández, G. (2016). New Trends in Opinion Mining Technologies in the Industry J. UCS Special Issue. Journal of Universal Computer Science, 22(5), 605-607.
Wang, S., Chen, Z., & Liu, B. (2016, April). Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model. In Proceedings of the 25th International Conference on World Wide Web (pp. 167-176). International World Wide Web Conferences Steering Committee.