Modeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis

Sentiment analysis: A survey on design framework, applications and future scopes

Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

A novel sentiment analysis method based on multi-scale deep learning

As the college students have been a most active user group in various social media, it remains significant to make effective sentiment analysis for college public opinions. Capturing the direction of public opinion in the student community in a timely manner and guiding students to develop the right values can help in the ideological management of universities. Universally, the recurrent neural networks have been the mainstream technology in terms of sentiment analysis. Nevertheless, the existing research works more emphasized semantic characteristics in vertical direction, yet failing to capture sematic characteristics in horizonal direction. In other words, it is supposed to increase more balance into sentiment analysis models. To remedy such gap, this paper presents a novel sentiment analysis method based on multi-scale deep learning for college public opinions. To fit for bidirectional semantic characteristics, a typical sequential neural network with two propagation paths is selected as the backbone. It is then extended with more layers in horizonal direction. Such design is able to balance both model depth and model breadth. At last, some experiments on a real-world social media dataset are conducted for evaluation, well acknowledging efficiency of the proposed analysis model.