ABSTRACT

In this paper, a novel sentiment analysis model is implemented, which consists of six stages: (i) Pre-processing, (ii) Keyword extraction and its sentiment categorisation, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Initially, the Mongodb documented tweets are subjected to pre-processing that includes steps such as stop word removal, stemming, and blank space removal. Accordingly, from the pre-processed tweets, the keywords are extracted. Based on the extracted keywords, the prevailing semantic words are extracted after classifying the sentimental keywords. Further, the evaluation of the semantic similarity score with the keywords takes place. Also, it exploits joint holoentropy and cross holoentropy. Here, the extraction of weighted holoentropy features is the main contribution, where a weight function is multiplied by the holoentropy features. To improve the performance of classification, a constant term is used for calculating weight function. It is tuned or optimised in such a way that the accuracy of the proposed method is better. The optimisation strategy uses the hybrid model that merges Particle Swarm Optimisation (PSO) into Whale Optimisation Algorithm (WOA). Hence, the proposed algorithm is named as Swarm Velocity-based WOA (SV-WOA). Finally, the analysis is done to prove the efficiency of the proposed model.

摘要

在本文中，实现了一种新颖的情感分析模型，该模型由六个阶段组成：（i）预处理，（ii）关键词提取及其情感分类，（iii）语义词提取，（iv）语义相似性检查，（ v) 特征提取，以及 (vi) 分类。最初，Mongodb 记录的推文要经过预处理，包括停用词删除、词干提取和空格删除等步骤。因此，从预处理的推文中提取关键词。根据提取的关键词，对情感关键词进行分类后，提取流行语义词。此外，还进行与关键词的语义相似度得分的评估。此外，它还利用联合全熵和交叉全熵。这里，加权全熵特征的提取是主要贡献，其中权重函数乘以全熵特征。为了提高分类的性能，使用常数项来计算权函数。它被调整或优化，使得所提出的方法的准确性更好。优化策略采用将粒子群优化（PSO）与鲸鱼优化算法（WOA）相结合的混合模型。因此，所提出的算法被命名为基于群体速度的WOA（SV-WOA）。最后进行分析，证明所提模型的有效性。它被调整或优化，使得所提出的方法的准确性更好。优化策略采用将粒子群优化（PSO）与鲸鱼优化算法（WOA）相结合的混合模型。因此，所提出的算法被命名为基于群体速度的WOA（SV-WOA）。最后进行分析，证明所提模型的有效性。它被调整或优化，使得所提出的方法的准确性更好。优化策略采用将粒子群优化（PSO）与鲸鱼优化算法（WOA）相结合的混合模型。因此，所提出的算法被命名为基于群体速度的WOA（SV-WOA）。最后进行分析，证明所提模型的有效性。