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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!
I’m very much interested in elaboration of this sentence:
“Sentences with subjective expressions are retained and that which conveys objective expressions are discarded.”
Why would be of interest to analyse sentences with subjective expressions, aren’t those conveying objective expressions of more value?
To elaborate my question.It is clear that sentences with subjective expressions hold writer’s stance that he is probably very passionate about, but if we consider product reviews where people often give negative review to a product on account of some shortcoming – that could be considered as objective comment and wouldn’t it have a value to a business owner to analyse those reviews as well? Therefore my question, why do we eliminate these sentences and should it be always done like that?
Hi Oli, in sentiment analysis there are two kinds of analysis- Polarity
and Emotional Analytics.
Polarity Analytics – it will be positive, negative or neutral.
Emotion Analytics – it comes under sad, happy, joy, anger, surprise, unknown.
According to the above two types of analytics, we don’t ignore anything. 100% we concentrate more on negative comments where user gives neutral or negative comments.The actual portion of the text which says negative is known as vibes. Vibes will actually give what is the actual form of text or a sentences which gives you negative or positive or neutral impression about the product.
E.g: “I don’t like the product” – Negative Vibe
Rather than whether sentence negative or positive, we actually find the information what makes the sentence negative and what makes the sentence positive.
Eg: “Product is really good” – Positive Vibe.
To be short nothing is omitted while you are doing the analysis. The one thing is that how you are going to clean this data and how you are going to apply this polarity, emotional analytics on your data which gives the better accuracy results of what form you want. If you go for a normal text mining or any sentiment analysis package, you will not get to the expectation what you need for the business. Definitely we need to go lot of customization to make sure that the entire thing is in line with your needs.
None of the sentiment analysis packages come in and which suits your business need. Only with the social media data if you go ahead directly and apply which is of generic in terms you can get those sentiment analysis package working but with little customization is required. When you want some in specific in terms of objective or subjective in the commands definitely you have to undergo lot of customization on text mining and sentiment analysis package.
wat is this 'R' language.