Personality Trait Recognition Solution Using Machine Learning — Overview
- A deep learning tool is called sentiment analysis examines a person’s writings or tweets. Based on the main sentiment, these messages or tweets can be loosely categorised as positive, negative, or neutral.
- Tweets that systems recognize patterns how to determine sentiment without manual intervention by educating algorithms with example of emotions in text.
- However, in order to properly appreciate a person’s personality, the description must be based on more accurate, in-the-moment sentiments like happiness, sadness, fury, love, perplexity, etc.
- Contemporary approaches to sentiment analysis are divided into three categories: wisdom, empirical, and hybrids.
- The most developed, successful, and often adopted methodology for sentiment analysis is the hybrid approach analysis model.
Benefits of Sentiment Analysis
- Easily identify customer sentiments.
- Improve the quality of service.
- Identify customer emotional triggers.
- Realtime indicators of customer emotion.
How have We Implemented Personality Trait Recognition Solution Using Machine Learning?
- Inside every tweet resides an explicit and implicit emotion. These tweets can be vastly classified into either Positive, Negative or Neutral based on the core sentiment.
- But to further understand the Personality of a person deeply, the classification must be based on real-time and more defined sentiments like Happiness, Sadness, Anger, Hate, Confused, etc.
- The contents of the tweet are pre-processed, and irrelevant data are dropped.
- The tweets along with their respective sentiment labels are split into Train and Test sets are then passed into 3 models, namely Universal Sentence Encoder (USE), LSTM and doc2vec model.
- The performance analysis is done on the models, and it is concluded that the USE model works the best among the available options.
Market Size: Sentiment Analysis
The global market for Sentiment Analysis Software estimated at US$1.6 Billion in the year 2020, is projected to reach a revised size of US$4.3 Billion by 2027, growing at a CAGR of 15.5% over the analysis period 2020–2027.