Abstract
Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, Sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university student's behavior towards the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, Glove, on the deep learning paradigms. Our model was trained on Google app reviews and tested on Student's App Reviews(SAR). The various combinations of these algorithms were compared amongst each other using F score and accuracy and inferences were highlighted graphically. SVM, amongst other classifiers, gave fruitful accuracy(93.41%), F score(89%) on bigram and TF IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.88% and 86.69% and F score of 86% and 78% respectively. Overall, LSTM on Glove embedding recorded the highest accuracy(95.2%) and F score(88%).
Abstract (translated)
谷歌应用商店通过评分和文本评论捕捉来自世界各地的用户思维，在一个多语言的竞技场中。由于其指数增长，无法手动提取评论中的信息。因此，通过机器学习和深度学习算法利用自然语言处理（NLP）进行情感分析和情感揭示，本研究对应用商店评论进行情感分类，并通过探索性分析识别出大学生对应用商店的态度。我们使用了TP、TF和TF IDF文本表示方案的机器学习算法，并使用Word嵌入、Glove在深度学习范式上进行比较。我们的模型在Google应用商店上进行训练，并在学生应用商店上进行测试。各种算法之间的组合通过F分数和准确度进行了比较，并突出了图形上的推理。SVM等分类器在词干和TF IDF方案上具有很高的准确率（93.41%），F分数（89%）。贝叶斯均衡增强了LR和NB的表现，准确度分别为87.88%和86.69%，F分数分别为86%和78%。总体而言，GloVe嵌入的LSTM记录了最高的准确率（95.2%）和F分数（88%）。
URL
https://arxiv.org/abs/2312.06705
