Sentiment analysis is analytical technique that uses statistics, natural language processing, and machine learning to determine the emotional meaning of communications. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services. Get the latest insights with our CIO Daily newsletter. One of the most prominent examples of sentiment analysis on the Web today is the Hedonometer, a project of the University of Vermont's Computational Story Lab.
In the sentiment analysis task, predicting the sentiment tendency of a sentence is an important branch. Previous research focused more on sentiment analysis in English, for example, analyzing the sentiment tendency of sentences based on Valence, Arousal, Dominance of sentences. the emotional tendency is different between the two languages. For example, the sentence order between Chinese and English may present different emotions. This paper tried a method that builds a domain-specific lexicon. In this way, the model can classify Chinese words with emotional tendency. In this approach, based on the , an ultra-dense space embedding table is trained through word embedding of Chinese TikTok review and emotional lexicon sources(seed words). The result of the model is a domain-specific lexicon, which presents the emotional tendency of words. I collected Chinese TikTok comments as training data. By comparing The training results with the PCA method to evaluate the performance of the model in Chinese sentiment classification, the results show that the model has done well in Chinese. The source code has released on github:https://github.com/h2222/douyin_comment_dataset
Sentiment analysis (or opinion mining) may be a natural processing technique want to determine whether data is positive, negative, or neutral. Sentiment analysis is usually performed on textual data to assist businesses to monitor brand and merchandise sentiment in customer feedback and understand customer needs. Sentiment analysis is that the process of detecting positive or negative sentiment in text. It's often employed by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an important tool to watch and understand that sentiment.
Call Center sentiment analysis is the processing of data by identifying the natural nuance of customer context and analyzing data to make customer service more empathetic. If you are employed in Call Center, the following scenario might be familiar: You get a call from a client and hear their words with stress. The cause for such a cataclysmic reaction: They got a bad rating for their products or business. Some of those reviews might be negative, formal, and neutral. Knowing what someone meant can be tricky unless you understand their emotional quotient.
Understanding the sentiments of the people is not easy unless they express their feelings, opinions and perspective anything. But if you have such platforms where people are freely speaking up about their thoughts and concerns, you can easily find out their sentiments. Here where Cogito comes in the facility of sentiment analysis. Sentiment Analysis is the process of determining the conceptions, judgments, feelings, opinions, viewpoints, conclusions, and other notions towards anything. It is a technique to analyze texts, images, emojis and various other actions to know what other people think about a product, service, company, brand name, or a reaction to a specific event, social movement, etc. Sentiment analysis is playing an enormous role in understanding people belonging to different groups and their sentiments.