ABSTRACT
Development in technology has led to a spike in sharing of opinions about different subjects on social media, for instance, movie or product reviews. Unprecedented COVID-19 led to forced isolation and affected mental health negatively. This paper introduces a system to detect users' emotions and mental states based on provided input. Among the different data sources available on social media, real-time Twitter data is used in this analysis. Sentiment analysis can be used as a tool at various levels, right from individual to organizational development. Deep learning algorithms like LSTM and CNN lay the foundation of this system. Python libraries and Google APIs are used to add functionalities. Earlier studies only focused on detecting emotions, whereas the proposed system provides the user with a graphical analysis of detected emotions and apt suggestions like motivational quotes or videos. The system accepts multilingual text input, speech, or video input. The scope of this system is not restricted to COVID-19 related texts. This research will assist individuals and businesses and aid future development. © 2022 IEEE.
ABSTRACT
This study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis. © 2022 IEEE.