No abstract

No abstract

One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side.

This study uses aspect level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site twitter from where a total of 4961 tweets about ten Patanjali branded products have been extracted and analysed. Along with the aspect level sentiment analysis, an opinion tagged corpora has also been developed. Machine learning approaches - Support Vector Machine (SVM), Decision Tree, and Naïve Bayes have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. Authors first identify customer preferences and / or opinions about a product or brand by analyisng online customer reviews as they express them on social media platform, twitter by using aspect level sentiment analysis. Authors also address the limitations of scarcity of opinion tagged data, required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.

Customer Relationship Management (CRM) is a systematic way of working with current and prospective customers to manage long-term relationships and interactions between the company and customers. Recently, Big Data has become a buzzword. It consists of huge data repositories, having information collected from online and offline resources, and it is hard to process such datasets with traditional data processing tools and techniques. The presented research work tries to explore the potential of Big Data to create, optimise and transform an insightful customer relationship management system by analysing large amount of datasets for enhancing customer life cycle profitability. In this research work, a dataset, “Book Crossing” is used for Big Data processing and execution time analysis for simple and complex SQL queries. This research tries to analyse the impact of data size on the query execution time for one of the majorly used Big Data frameworks, namely Apache Spark. It is a recently developed in-memory Big Data processing framework with a SPARK SQL module for efficient SQL query execution. It has been found that Apache-Spark gives better results with large size datasets compare to small size datasets and fares better as compared to Hadoop, one of the majorly used Big Data Frameworks (based on qualitative analysis).