NLP and text analysis are AI techniques for isolating, selecting and measuring the affective nature of unstructured information. With these techniques, sentiment analysis is applied for understanding customer feedback (reviews, surveys), analyzing social media traffic, creating website content and shaping marketing campaign messaging.
Sentiment Analysis Techniques
A scored word list is a supervised learning technique for completing sentiment analysis. The scored list or lexicon contains words with scores from most negative to most positive. Numbers assigned depend on the lexicon being used, for example AFINN scores range from -4 to +4. Text is decomposed into its individual words. The individual words are matched against the lexicon, summed and divided by the number of words to get an average score. Lexicons can be developed using surveys and adjusted to address specific domains. This approach can be enhanced with a rules base to address other language features, such as context, multipolarity and negation resulting in a compound calculated score for a piece of text. The power of this approach is understanding the affect of each word and being able to adjust the rules.
Using clustering, unsupervised learning can also be used for sentiment analysis. Like any unsupervised technique data is fed into the model, in this case, text, and in real time the model returns the overall results of positive, neutral or negative. To build the module representative embeddings must be isolated and scored. Clustering is then completed on the data. The relative location of words in a cluster determines their positive or negative value. Then each word is considered for how unique it is in each sentience. This along with the sentiment scores are used to complete sentence scoring.
Sentiment Analysis Application
Some applications of sentiment analysis include:
Content Creation – confirming intended sentiment for social media and website content
Customer Feedback – analyzing market sentiment towards products and/or services
Product Review – capturing the most valued product features
Brand Image – monitoring social media by segment for sentiments on brand
Stock Market – real time assessment of investor sentiment on stocks influencing long/short positions
Regulatory Compliance – isolating product applicable in from compliance document data bases
Competitor Intelligence – comparing sentiment on social media against competitors
Sentimental Analysis Value
Sentiment analysis confirms the intent of communications: emails, website content and social media postings. This contributes to the reach, impressions and engagement of these communications. With studies indicating positive emotions increase the speed and reach of social messages, sentiment analysis has significant value. In addition to social media applications, sentiment analysis also provides value in tracking customer service feedback and product reviews.