WhatsApp Chat Sentiment Analysis in R

What is Sentiment Analysis

What is Sentiment Analysis.

Sentiment analysis is a type of data mining that measures the inclination of people’s opinions through natural language processing (NLP), computational linguistics and text analysis, which is later used to extract and analyze subjective information from the internet - mostly social media and similar sources. The analyzed data quantifies the public’s sentiments or reactions toward certain products, people or ideas and reveal the contextual polarity of the information.

Sentiment analysis uses data mining processes and techniques to extract and capture data for analysis in order to decide the subjective opinion of a document or collection of documents or information, like blog posts, reviews, news articles, and social media feeds like tweets and status updates.

Sentiment analysis allows organizations to track the following:

1. Brand reception and popularity by the target audience

2. New product perception and anticipation by the target audience

3. Company reputation in the market

4. Flame/rant detection

Sentiment analysis is also known as opinion mining. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The applications of sentiment analysis are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organizations across the world because it further helps in understanding the consumer more. Shifts in sentiment on social media correlate with shifts in the stock market.

For example;

Expedia Canada took advantage of the ability to quickly understand consumer attitudes and react accordingly when they noticed that there was a steady increase in negative feedback to the music used in one of their television adverts. Sentiment analysis conducted by the brand revealed that the music played on the commercial had become incredibly irritating after multiple airings, and consumers were flocking to social media to vent their frustrations. A couple of weeks after the advert first aired, over half of the online conversation about the campaign was negative. Rather than labeling the advert as a failure, Expedia was able to address the negative sentiment in a playful and self-knowing way by airing a new version of the advert, which featured the offending violin being smashed.

Sentiment Analysis Use Cases

Sentiment Analysis for Brand Monitoring

One of the most well-documented uses of Sentiment Analysis is to get a full 360 view of how your brand, product, or company is viewed by your customers and stakeholders. Widely available media, like product reviews and social, can reveal key insights about what your business is doing right or wrong. Companies can also use sentiment analysis to measure the impact of a new product, ad campaign, or consumer’s response to recent company news on social media.

Sentiment Analysis for Customer Service

Customer service agents often use sentiment analysis to automatically sort incoming user email into “urgent” or “not urgent” buckets based on the sentiment of the email, proactively identifying frustrated users. The agent then directs their time toward resolving the users with the most urgent needs first. As customer service becomes more and more automated through Machine Learning, understanding the sentiment of a given case becomes increasingly important.

Sentiment Analysis for Market Research and Analysis

Sentiment analysis is used in business intelligence to understand the subjective reasons why consumers are or are not responding to something. Sentiment analysis can also be used in the areas of political science, sociology, and psychology to analyze trends, ideological bias, opinions, gauge reactions, etc.

Many of these applications are already up and running. Bing recently integrated sentiment analysis into its Multi-Perspective Answers product. Hedge funds are almost certainly using the technology to predict price fluctuations based on public sentiment.