2018/03/06 by Wissam Siblini, Siblini, Wissam, Frank Meyer +3
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection #Text and Document Classification Technologies #cs.IR
paper · pdf · doi:10.48550/arxiv.1803.02101
8 pages
arxiv created 2018/03/06 · openalex publication_date 2018/03/06 · arxiv updated 2018/03/07 · openalex created_date 2020/02/07 · openalex updated_date 2026/07/28
This paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results.