Sentiment Analysis refers to identifying and categorizing opinions in a particular text to determine various things like the attitude of the writer in writing on a particular product or topic. Sentiment Analysis studies, analyses, and deals with the subjective information provided. It picks out the key points of expression like opinions, appraisals, emotions, attitudes, etc. are positive, negative or neutral classifications.
Sentiment Analysis is also known as opinion mining which means determining the emotional expression in a sentence.
Sentiment Analysis is mostly useful in social media monitoring through various social media monitoring tools. It allows us to gain a wider aspect of the public opinions behind the reviews, comments, and opinions. There are powerful applications of sentiment analysis which has the ability to extract insights.
Sentiment Analysis is the domain of understanding the emotions with the help of a software. It is mostly used by the developers and businessmen.
- Sentiment analysis for brand monitoring: Sentiment analysis shows a total view of your brand, product and company and how it is viewed by your customers. Thus, sentiment analysis plays a vital role in monitoring your brand from all the aspects and lets you improve the weak points related to your brand.
- Sentiment analysis for customer service: The agents of sentiment analysis take part in this sector. They analyze the sentiment of the mails and arrange them from urgent to later. They then direct their timing accordingly for resolving the urgent issues first.
- Sentiment analysis for market research: Sentiment analysis, in its basic sense, is used to analyze sentiments of consumers or customers in a business or market and lets you know the reasons why the consumers aren’t responding to some product. Sentiment analysis can also be used in various other fields rather than just brand monitoring, customer service, market research, etc. Thus, sentiment analysis has a number of applications including customer interactions.
- R Sentiment Analysis: Sentiment analysis using R language is usually known as R sentiment analysis. R is a programming language and also a freely available software for statistical computation.
Why use R for Sentiment Analysis?
- Well, just because it is a language known to nearly all of the academicians, it is the most popular language. Most of the researchers use R to experiment with Data Science. So, all in all, more people are inclined towards using R.
- Secondly, R has a great interface and graphics. The R has two packages namely, ggplot2 and ggedit that have become the most common standard for plotting packages. While the ggplot2 package focuses upon visualizing the data, ggedit helps people in bridging the gap between preparing a plot and correcting the annoying aesthetics of the plot.
- Thirdly, R is a language built specifically for Data Science and Data analysis. All of the R libraries aim towards a single thing i.e making Data Analysis easier, detailed and more approachable. Even the R community is very active and supportive and they have great knowledge in both Statistics and Programming.
- Last but not the least, R is open to Machine Learning which is at some point very necessary in Sentiment Analysis. R provides surplus tools to Analysts to train and analyze an algorithm and predict future events. The list of packages in R for ML is really extensive. Thus, R makes ML much more approachable.