Sentiment Analysis On News Data
Sentiment classification is part of the large category of text classification tasks where classifier uses a phrase, or a list of phrases and detect the sentiment behind it and categorize into positive, negative or neutral. Sometimes, the third attribute is not taken to keep it a binary classification problem. In recent tasks, sentiments like "somewhat positive" and "somewhat negative" are also being considered. It has become one of the hottest topics in the field because of its relevance in real world's text.
NLP is not a new field and neither is machine learning but combination of of these two is quite modern and progressing to be more effective in the future. Smart phones are already using it in keyboard word suggestion, intelligent auto-completion etc. Sentiment Analysis or Opinion Mining refers to the use of NLP, text analysis and computational linguistics to determine subjective information or the emotional state of the writer/subject/topic. It is commonly used in reviews to save businesses a lot of time from manually reading comments.
Features
Our sentiment analysis system is capable to collect and analyze publicly available information from different online sources. It is automated process of analyzing text data and classifying it as positive, negative or neutral.
Usually, the world’s data is unstructured and not organized in a pre-defined manner. Our sentiment analysis system allows us to make sense of this abandon unstructured text by saving hours of manual data processing and by making teams more efficient. Our system not only does analysis in only positive or negative categories but also defines its intensity score i.e. positivity with x percent, negativity with y percent and neutral with z percentage.