Sentiment Analysis 101
Sentiment analysis can be incredibly useful, and can help companies better answer pertinent questions and gain valuable business insights. Sentiment analysis technologies will continue to improve as they become more widely adopted. But what can sentiment analysis do for you?
By Scott Sims, Buzzlogix.
Sentiment analysis is a term that refers to the use of natural language processing, text analysis, and computational linguistics in order to ascertain the attitude of a speaker or writer toward a specific topic.
Basically, it helps to determine whether a text is expressing sentiments that are positive, negative, or neutral. Sentiment analysis is an excellent way to discover how people, particularly consumers, feel about a particular topic, product, or idea.
The origin of sentiment analysis can be traced to the 1950s, when sentiment analysis was primarily used on written paper documents. Today, however, sentiment analysis is widely used to mine subjective information from content on the Internet, including texts, tweets, blogs, social media, news articles, reviews, and comments. This is done using a variety of different techniques, including NLP, statistics, and machine learning methods. Organizations then use the information mined to identify new opportunities and better target their message toward their target demographics. The Obama Administration even uses sentiment analysis to predict public response to its policy announcements.
What Are the Different Types of Sentiment Analysis?
Sentiments refer to attitudes, opinions, and emotions. In other words, they are subjective impressions as opposed to objective facts. Different types of sentiment analysis use different strategies and techniques to identify the sentiments contained in a particular text. There are two main types of sentiment analysis: subjectivity/objectivity identification and feature/aspect-based sentiment analysis.
Subjectivity/Objectivity Identification: Subjectivity/objectivity identification entails classifying a sentence or a fragment of text into one of two categories: subjective or objectivity. However, it should be noted that there are challenges when it comes to conducting this type of analysis. The main challenge is that the meaning of the word or even a phrase is often contingent on its context.
Feature/Aspect-Based Identification: Feature/aspect identification allows for the determination of different opinions or sentiments (features) in relation to different aspects of an entity. Unlike subjectivity/objectivity identification, feature/aspect based identification allows for a much more nuanced overview of opinions and feelings.
How Can Sentiment Analysis Be Used?
Sentiment analysis has a number of different uses. Most notably, with the rise of social media sites like Facebook and Twitter, the increased popularity of blogs, and the rise in review, rating, and recommendation sites, companies are becoming increasingly interested in sentiment analysis. With consumers able to share their opinions across the web so easily, online opinions have become a valuable currency for businesses and companies trying to cultivate their digital reputations, identify new opportunities, and successfully market their products. But with so much information out there, it can be hard for companies to hone in on the most valuable parts of consumers’ comments.
That’s why sentimental analysis is so useful. Organizations are leveraging the power of sentiment analysis to filter out this valuable information in order to better understand consumers’ conversation and take more effective, better-targeted action. Today, organizations are using sentiment analysis for everything from marketing to customer service. Some of the many different applications for sentiment analysis include:
Social Media Monitoring: In today’s world, the vast majority of social media tools have some kind of sentiment analysis capabilities. Companies also use automated sentiment analysis based on word lists, with each word being given a pre-defined sentiment value. The value of the text is then determined based on the words it contains. This has a number of different uses. For example, a restaurant might engage in social media monitoring in order to gauge how people feel about their menu, figure out whether or not people enjoyed their food, and ascertain what feelings people associated with their overall experience at the restaurant. And the good news is that the accuracy rates of sentiment analysis for social media monitoring keep getting better. Sentiment analysis companies achieve accuracy rates of over 75 percent with its automated sentiment analysis capabilities.
Furthermore, social media monitoring is also an excellent way to better identify your brand’s influencers and promoters. For example, let’s say you need to figure how where negative content about your brand is coming from. You could identify, say, 50 of the major influencers in your industry and then analyze sentiment of their tweets about your brand, to figure out who has negative perceptions. From there, you can reach out to the influencers individually, and hopefully change their perception.
Public Relations: Sentiment analysis can also help companies develop and refine their public relations strategy. For example, companies can use sentiment analysis to identify sales leads and spot industry trends. As previously mentioned, sentiment analysis can also be used to identify influencers in your industry with positive sentiments toward your brand, which can be leveraged in a PR strategy.
Marketing: Companies are increasingly using the information found in customer-generated content on product reviews and social media sites. For example, let’s say that Samsung wants to know how consumers feel about its new Galaxy phone. Instead of conducting a survey, analysts can go online and evaluate the comments customers have left on major online ecommerce sites like Amazon. Samsung can analyze the content of these reviews. For example, Samsung could determine the tone of comments being left to gain insight into the emotions consumers feel toward the product or analyze the comments to figure out how much knowledge customers have about the product.
Data Mining: Sentiment analysis can also be used for data mining, or gathering competitive intelligence about your competitors. For example, a brand could easily track social media mentions or mentions of competitors in other places across the web, and analyze how consumers feel about the competitors and their products. This is an excellent way to gain a competitive edge in today’s highly competitive marketplace.
Political Analysis: Studies of sentient analysis of tweets and microblogs have shown that such analysis can accurately indicate political sentiment. In a research paper entitled “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” four researchers at the Technical University of Munich found that tweets do indeed accurately reflect voter preferences, and that the “the sentiment of Twitter messages closely corresponds to political programs, candidate profiles, and evidence from the media coverage of the campaign trail.” After analyzing over 100,000 tweets that mentioned parties or politicians in the months leading up to Germany’s 2009 federal election, the researchers found that “the mere number of messages reflects the election result and even comes close to traditional election polls.”
There are still a number of different challenges when it comes to effectively conducting sentiment analysis. First and foremost, people tend to express their opinions in complex ways, which can make it difficult to identify a clear sentiment. Furthermore, in an opinion text, lexical content alone can make it difficult to discern the opinion being expressed, while rhetorical devises such as irony and sarcasm can also make it more difficult to clearly identify sentiment. However, as technology continues to improve, it will become easier and easier to overcome these challenges.
The bottom line is that in today’s world, sentiment analysis can be incredibly useful, helping companies to better answer a number of pertinent business questions, from “why don’t people like this new product?” to “how can I more effectively market this new service to my target demographic?” Sentiment analysis is all about helping companies gain better insights into their customers, and helping them to bridge the gap between insight and action. And overall, sentiment analysis capabilities will only to continue to grow and expand, making companies more likely to widely adopt sentiment analysis as a means of adding value to their organizations.
Bio: Scott Sims, is the CEO and Co-Founder of Buzzlogix. A serial entrepreneur with a strong technological background, he is responsible for leading Buzzlogix’s worldwide operations, and for steering the overall direction and strategy of the company.
Original.
Related:
Sentiment analysis is a term that refers to the use of natural language processing, text analysis, and computational linguistics in order to ascertain the attitude of a speaker or writer toward a specific topic.
Basically, it helps to determine whether a text is expressing sentiments that are positive, negative, or neutral. Sentiment analysis is an excellent way to discover how people, particularly consumers, feel about a particular topic, product, or idea.
The origin of sentiment analysis can be traced to the 1950s, when sentiment analysis was primarily used on written paper documents. Today, however, sentiment analysis is widely used to mine subjective information from content on the Internet, including texts, tweets, blogs, social media, news articles, reviews, and comments. This is done using a variety of different techniques, including NLP, statistics, and machine learning methods. Organizations then use the information mined to identify new opportunities and better target their message toward their target demographics. The Obama Administration even uses sentiment analysis to predict public response to its policy announcements.
What Are the Different Types of Sentiment Analysis?
Sentiments refer to attitudes, opinions, and emotions. In other words, they are subjective impressions as opposed to objective facts. Different types of sentiment analysis use different strategies and techniques to identify the sentiments contained in a particular text. There are two main types of sentiment analysis: subjectivity/objectivity identification and feature/aspect-based sentiment analysis.
Subjectivity/Objectivity Identification: Subjectivity/objectivity identification entails classifying a sentence or a fragment of text into one of two categories: subjective or objectivity. However, it should be noted that there are challenges when it comes to conducting this type of analysis. The main challenge is that the meaning of the word or even a phrase is often contingent on its context.
Feature/Aspect-Based Identification: Feature/aspect identification allows for the determination of different opinions or sentiments (features) in relation to different aspects of an entity. Unlike subjectivity/objectivity identification, feature/aspect based identification allows for a much more nuanced overview of opinions and feelings.
How Can Sentiment Analysis Be Used?
Sentiment analysis has a number of different uses. Most notably, with the rise of social media sites like Facebook and Twitter, the increased popularity of blogs, and the rise in review, rating, and recommendation sites, companies are becoming increasingly interested in sentiment analysis. With consumers able to share their opinions across the web so easily, online opinions have become a valuable currency for businesses and companies trying to cultivate their digital reputations, identify new opportunities, and successfully market their products. But with so much information out there, it can be hard for companies to hone in on the most valuable parts of consumers’ comments.
That’s why sentimental analysis is so useful. Organizations are leveraging the power of sentiment analysis to filter out this valuable information in order to better understand consumers’ conversation and take more effective, better-targeted action. Today, organizations are using sentiment analysis for everything from marketing to customer service. Some of the many different applications for sentiment analysis include:
Social Media Monitoring: In today’s world, the vast majority of social media tools have some kind of sentiment analysis capabilities. Companies also use automated sentiment analysis based on word lists, with each word being given a pre-defined sentiment value. The value of the text is then determined based on the words it contains. This has a number of different uses. For example, a restaurant might engage in social media monitoring in order to gauge how people feel about their menu, figure out whether or not people enjoyed their food, and ascertain what feelings people associated with their overall experience at the restaurant. And the good news is that the accuracy rates of sentiment analysis for social media monitoring keep getting better. Sentiment analysis companies achieve accuracy rates of over 75 percent with its automated sentiment analysis capabilities.
Furthermore, social media monitoring is also an excellent way to better identify your brand’s influencers and promoters. For example, let’s say you need to figure how where negative content about your brand is coming from. You could identify, say, 50 of the major influencers in your industry and then analyze sentiment of their tweets about your brand, to figure out who has negative perceptions. From there, you can reach out to the influencers individually, and hopefully change their perception.
Public Relations: Sentiment analysis can also help companies develop and refine their public relations strategy. For example, companies can use sentiment analysis to identify sales leads and spot industry trends. As previously mentioned, sentiment analysis can also be used to identify influencers in your industry with positive sentiments toward your brand, which can be leveraged in a PR strategy.
Marketing: Companies are increasingly using the information found in customer-generated content on product reviews and social media sites. For example, let’s say that Samsung wants to know how consumers feel about its new Galaxy phone. Instead of conducting a survey, analysts can go online and evaluate the comments customers have left on major online ecommerce sites like Amazon. Samsung can analyze the content of these reviews. For example, Samsung could determine the tone of comments being left to gain insight into the emotions consumers feel toward the product or analyze the comments to figure out how much knowledge customers have about the product.
Data Mining: Sentiment analysis can also be used for data mining, or gathering competitive intelligence about your competitors. For example, a brand could easily track social media mentions or mentions of competitors in other places across the web, and analyze how consumers feel about the competitors and their products. This is an excellent way to gain a competitive edge in today’s highly competitive marketplace.
Political Analysis: Studies of sentient analysis of tweets and microblogs have shown that such analysis can accurately indicate political sentiment. In a research paper entitled “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” four researchers at the Technical University of Munich found that tweets do indeed accurately reflect voter preferences, and that the “the sentiment of Twitter messages closely corresponds to political programs, candidate profiles, and evidence from the media coverage of the campaign trail.” After analyzing over 100,000 tweets that mentioned parties or politicians in the months leading up to Germany’s 2009 federal election, the researchers found that “the mere number of messages reflects the election result and even comes close to traditional election polls.”
There are still a number of different challenges when it comes to effectively conducting sentiment analysis. First and foremost, people tend to express their opinions in complex ways, which can make it difficult to identify a clear sentiment. Furthermore, in an opinion text, lexical content alone can make it difficult to discern the opinion being expressed, while rhetorical devises such as irony and sarcasm can also make it more difficult to clearly identify sentiment. However, as technology continues to improve, it will become easier and easier to overcome these challenges.
The bottom line is that in today’s world, sentiment analysis can be incredibly useful, helping companies to better answer a number of pertinent business questions, from “why don’t people like this new product?” to “how can I more effectively market this new service to my target demographic?” Sentiment analysis is all about helping companies gain better insights into their customers, and helping them to bridge the gap between insight and action. And overall, sentiment analysis capabilities will only to continue to grow and expand, making companies more likely to widely adopt sentiment analysis as a means of adding value to their organizations.
Bio: Scott Sims, is the CEO and Co-Founder of Buzzlogix. A serial entrepreneur with a strong technological background, he is responsible for leading Buzzlogix’s worldwide operations, and for steering the overall direction and strategy of the company.
Original.
Related: