Social media is the new knowledge hub for all age groups. It has become a platform to express sentiments in the form of opinions and reviews on almost everything- movies, brands, product, social – activities and so on. The reviews or opinions can be positive or negative and analyzing the same is known as ‘Sentiment Analysis’.
“Sentiment Analysis can be defined as a systematic analysis of online expressions. “
Sentiment Analysis is greatly used in R, an open source tool for comprehensive statistical analysis. R performs the important task of Sentiment Analysis and provides visual representation of this analysis. For a comprehensive explanation, read our post on Business Analytics with R and Reasons to learn R. There are plenty of reasons on why a Marketer should go for R, as he is one of the people who will greatly benefit from R
In our previous post, we covered types of sentiment analysis and the scenarios it’s used in. The next big question here is; how can an organization actually analyze the sentiment data?
There are 5 steps to analyze sentiment data and here’s the graphical representation of the methodology to do the same.
Methods of Sentiment Analysis
- Data Collection
Consumers usually express their sentiments on public forums like the blogs, discussion boards, product reviews as well as on their private logs – Social network sites like Facebook and Twitter. Opinions and feelings are expressed in different way, with different vocabulary, context of writing, usage of short forms and slang, making the data huge and disorganized. Manual analysis of sentiment data is virtually impossible. Therefore, special programming languages like ‘R’ are used to process and analyze the data.
- Text Preparation
Text preparation is nothing but filtering the extracted data before analysis. It includes identifying and eliminating non-textual content and content that is irrelevant to the area of study from the data.
- Sentiment Detection
At this stage, each sentence of the review and opinion is examined for subjectivity. Sentences with subjective expressions are retained and that which conveys objective expressions are discarded. Sentiment analysis is done at different levels using common computational techniques like Unigrams, lemmas, negation and so on.
- Sentiment Classification
Sentiments can be broadly classified into two groups, positive and negative. At this stage of sentiment analysis methodology, each subjective sentence detected is classified into groups-positive, negative, good, bad, like, dislike.
- Presentation of Output
The main idea of sentiment analysis is to convert unstructured text into meaningful information. After the completion of analysis, the text results are displayed on graphs like pie chart, bar chart and line graphs.
Carrying out sentiment analysis is an important task for all the product and service providers today. So, use ‘R’ language and get started!