2015/09/08 by Rishabh Soni, Soni, Rishabh, K. James Mathai +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Text and Document Classification Technologies #Web Data Mining and Analysis #cs.CL #cs.IR #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.1509.02437
5 pages
arxiv created 2015/09/08 · openalex publication_date 2015/09/08 · arxiv updated 2015/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Over the past decade humans have experienced exponential growth in the use of online resources, in particular social media and microblogging websites such as Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line, etc. Many companies have identified these resources as a rich mine of marketing knowledge. This knowledge provides valuable feedback which allows them to further develop the next generation of their product. In this paper, sentiment analysis of a product is performed by extracting tweets about that product and classifying the tweets showing it as positive and negative sentiment. The authors propose a hybrid approach which combines unsupervised learning in the form of K-means clustering to cluster the tweets and then performing supervised learning methods such as Decision Trees and Support Vector Machines for classification.