论文标题
应用程序评论的比较情绪分析
Comparative Sentiment Analysis of App Reviews
论文作者
论文摘要
Google App Market通过评级和文本评论捕获了用户的思想学院。批评对应用程序的观点与其满意度成正比。因此，这可以帮助其他用户在下载或购买应用程序之前获得见解。由于指数的增长，无法手动提取评论中的潜在信息。通过使用NLP的机器学习算法，情感分析用于明确发现和解释情绪。这项研究旨在对应用程序评论进行情感分类，并确定大学生对应用程序市场的行为。我们使用TF-IDF文本表示方案应用了机器学习算法，并在集合学习方法上评估了性能。我们的模型接受了Google评论的培训，并对学生的评论进行了测试。 SVM记录了Tri-gram + TF-IDF方案上的最大精度（93.37 \％），F-评分（0.88）。装袋的精度分别为87.80 \％和85.5 \％，提高了LR和NB的性能。
Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before downloading or purchasing the apps. The potential information from the reviews can't be extracted manually, due to its exponential growth. Sentiment analysis, by machine learning algorithms employing NLP, is used to explicitly uncover and interpret the emotions. This study aims to perform the sentiment classification of the app reviews and identify the university students' behavior towards the app market. We applied machine learning algorithms using the TF-IDF text representation scheme and the performance was evaluated on the ensemble learning method. Our model was trained on Google reviews and tested on students' reviews. SVM recorded the maximum accuracy(93.37\%), F-score(0.88) on tri-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.80\% and 85.5\% respectively.
