Overview of Sentiment and Intent Analysis
- Sentiment Analysis is termed as contextual mining of text to identify and extract information, understand the social sentiment of a brand. It is a text classification tool to analyze incoming messages and to depict positive, negative or neutral sentiments.
- Sentiment Analysis using Natural Language Processing involves Supervised Learning, Neural Network Approach. Sentiment Analysis using Deep Learning will include Visual Keras Deep Learning Approach.
- Intent Analysis involves understanding the emotions and intent of a user. It involves choosing the right events, tracking behavior against retention, identification of user’s need, bringing Real-Time Data Insights deriving value from Predictive Analytics. Intent Analysis using Automated Text Classification with Machine Learning involves Supervised Text Classification, Unsupervised Text Classification. Intent Analysis using Deep Learning involves Convolutional Neural Networks.
Business Challenge for Sentiment Analysis Adoption
- Sarcasm Detection
- Evaluate text to predict emotions
- Parallel Computing for Massive Data
- To improve algorithm precision
Solution Offered for Real Time Analysis
Real-Time Solution focusing Twitter trends and tweets involving –
- Web Scraping to crawl Data from Twitter using Tweepy in Python.
- Natural Language Processing to clean Textual Data and Feature Extraction.
The various steps included are –
- Sentence Tokenization
- Word Tokenization
- Regular Expressions
- Removing Stopwords
- Working on n-grams
Algorithms and Models use Supervised Learning algorithms in Text Mining trained on massive volume of data for better feature extraction and better accuracy to predict one’s attribute.