从Google应用评论中感知大学生的观点
Perceiving University Student's Opinions from Google App Reviews
AI总结：
本研究针对Google应用评论数量庞大难以人工分析的问题，结合多种机器学习、集成学习及深度学习方法开展情感分类，在学生应用评论数据集上验证了基于Glove嵌入的LSTM性能最优，可用于分析大学生对应用市场的行为倾向。
摘要：
Google应用市场在多语言环境中，通过评分和文本评论收集了全球各地用户的思想流派。由于评论数量呈指数级增长，无法手动提取其中的潜在信息。因此，采用结合NLP的机器学习与深度学习算法进行的情感分析，能够明确揭示并解读用户情绪。本研究对应用评论开展情感分类，并通过探索性分析识别大学生对应用市场的行为倾向。我们使用TP、TF以及TF-IDF文本表示方案应用机器学习算法，并在集成学习方法Bagging上评估其性能；在深度学习范式中则采用词嵌入Glove。我们的模型在Google应用评论上训练，并在Student's App Reviews（SAR，学生应用评论数据集）上测试。我们使用F值和准确率对这些算法的多种组合进行了相互比较，并以图形方式突出了推论结果。在所有分类器中，SVM在二元语法（bigram）和TF-IDF方案下取得了优异的准确率（93.41%）和F值（89%）。Bagging提升了LR和NB的性能，二者准确率分别为87.88%和86.69%，F值分别为86%和78%。总体而言，基于Glove嵌入的LSTM取得了最高的准确率（95.2%）和F值（88%）。
英文摘要：
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%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
