Feature based opinion analysis on social media tweets with association rule mining and multi‐objective evolutionary algorithms

Abstract

Social media platform has achieved wide popularity in presenting the user-generated information online. The proliferation of user-generated content through social networking sites can enhance the existing transportation system. This work adds to the existing research by proposing a novel system to assess transit rider's reviews on the quality of transport services using Twitter information. A novel framework of a multi-objective evolutionary approach with association rule mining is proposed for feature-based opinion analysis on social media transit reviews. Feature-based opinion analysis is performed in two steps such as feature extraction and opinion analysis.First, the corpus of association rules is generated with opinion analysis, morphological and syntactic analysis, and semantic representation. Then, multi-objective flower pollination, multi-objective gray wolf, multi-objective moth flame, and multi-objective cat swarm optimization are applied to discover high-quality association rules on the transit user's opinion. The experimental results indicate that multi-objective cat swarm optimization using association rule mining performs better in terms of confidence, coverage, the interestingness of the rules, computational time, mean average support, and mean average confidence than the other proposed as existing approaches. It is observed that MCSO-ARM achieved an improvement in the range of 0.21%-0.27%

Little Known POI Category Estimation via Syntactical Knowledge Graph Generated via Tweets

“…what to eat in Fukuoka? : eat Fukuoka's Karashi Mentaiko (219), eat motsunabe at Rakutenchi (42), eat ramen in Fukuoka (25).…”

“…Similar approaches are presented in [22] [23], where the authors analyze tweets and predict political sentiment. An approach with the same aim extracts tweet features via adjectives and verbs to identify sentiment terms [24] [25]. The authors defined a set of rules to classify tweets according to the detected sentiment.…”

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

“…They first generated a corpus of association rules and applied a multi-objective flower pollination algorithm to discover association rules for user opinions. The experimental results showed that their multi-objective cat swarm optimization algorithm outperformed other existing methods in terms of confidence and computational time [8]. Kota et al proposed a processing method for sentiment analysis datasets by combining LSTM and attention methods.…”

“…Through the above steps, the algorithm can build an efficient electrical data set model [24]. In the calculation steps of power grid data, the calculation method used in this study is power flow calculation, which follows Equation (8).…”

Performance Analysis of Multi-Threaded Distributed Evolutionary Algorithms for Image-Processing Applications

“…Liu et al, recommended a collaborative dragonfly algorithm with a novel communication strategy, for the segmentation of multi-thresholding color images [31]. Meesala et al, proffered a feature-based emotion detection in social media, using multiobjective EAs [32]. Luo's group recommended a Whale Optimization Algorithm to reduce noise present in an image [33].…”