# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 5 Example of Applications  
Sentiment analysis [45; 46] is one of many NLP applications using WSD. Sentiment analysis is to identify the subjective information that reflects people's opinions, attitudes, and feelings regarding something or someone. Each word has different polarity scores depending on its sense. For example, the word \"breakdown\" in the context of \"a mental or physical breakdown\" conveys negative polarity whereas \"breakdown\" in the context of \"an analysis into mutually exclusive categories\" conveys no polarity. The instant way to retrieve polarity scores is from a lexicon resource for sentiment analysis and opinion mining called SentiWordNet [47] by providing it, the correct sense (the synset from WordNet) as an input. SentiWordNet developed based on quantitative analysis of the definitions associated with the synsets and semi-supervised classifications. Training dataset started with a small amount of manual annotation and then using WordNet to crawl along the associated synsets for bootstrapping training dataset. Three numerical scores (i.e., objective score, positive score, and negative score) are produced by combining the results of a committee of eight ternary classifications. SentiWordNet is reported as an efficiently performing lexical resource for sentiment analysis of microblog posts [48]. Therefore, WSD is the important pre-processing step, and its accuracy directly affects the result of lexicon-based sentiment analysis. WSD and SentiWordNet have been used in many research works related with social media analytics [49], e.g., [PERSON] et al. [50] discovered insights from Twitter by sentiment analysis, [PERSON] et al. [51] proposed a scalable semantic-based sentiment analysis methodology.  
Moreover, WSD could be employed in the feature extraction process for supervised machine learning approaches, e.g., in this work, \"Beyond Binary Labels: Political Ideology Prediction of Twitter Users\" [52]. They used a broad range of linguistic features to help in prediction; one of them is the sentiment and emotion feature by quantifying emotion terms from Twitter posts using lexicon of words associated with six basic emotions (i.e., anger, disgust, fear, joy, sadness, and surprise) as well as general positive and negative sentiment. As mentioned earlier, words have different meanings and also convey different sentiment orientations. WSD could help in the process to automatically obtain the correct sentiment orientation of the terms relying on the context. WSD could help in data collection and preprocessing as well; e.g., in \"Fortune Teller: Predicting Your Career Path\" [53], they manually collected information about careers by searching for keywords corresponding to the career paths from different social network accounts such as Twitter, Facebook and Linkedin, grouped similar careers and then predicted a career path. WSD can distinguish different careers automatically. For example,with the keyword \"doctor\", there are two-word senses associated with career \"doctor\": i.e., a licensed medical practitioner and a person who holds a Ph.D. degree. WSD will group similar social network accounts by computing the similarity between the curriculum vitae and word sense definitions.