Updated March 14, 2023
Sentiment analysis has become a powerful tool for businesses wanting to know what their customers are saying on social media. By analyzing the emotions expressed in customer posts and reviews, businesses can gain valuable insights into who their customers are and how they feel about their products. In this guide, you’ll learn how to use sentiment analysis to extract useful information from social media posts.
Introduction to Sentiment Analysis in Social Media
In the business world, brands and their promoters are keen to know what others think of the company and the brand. It is achieved through sentiment analysis. Sentiment analysis has become automated thanks to the enormity of the task and the new tools that have emerged to make it easier. Long ago, it wasn’t easy to measure sentiments about the company. Still, now feedback is quite instantaneous thanks to the wide reach of sentiment analysis in social media – it includes customer voices, opinions, product reviews, news, and analysis.
Sentiment analysis in social media is usually done on the basis of references to the company or brand on the web, in print, in electronic media, and in the news. Sentiment analysis in social media not only helps companies/marketers understand what others are thinking about them, but it also helps in analyzing such data and taking remedial action as required on its basis. It is also used to monitor content (Inbound marketing) initiatives and how it is impacting the perception of the company.
Top Strategies (Social Media)
Here are 8 strategies to make the best use of sentiment analysis in social analysis and how best to utilize the tools available for it.
1. Make your sentiment analysis in social media as possible
Sentiment analysis would become meaningful only when it is done on a comprehensive scale. It should cover social media, your own CRM data (Customer Relationship Management), websites, news, blogs, and so on. This is possible with various tools available, which are either subscription-driven or free.
The analysis should be done across Twitter, Facebook, Pinterest, Google+., Quora, LinkedIn, YouTube, Slideshare, Instagram, and LinkedIn. There should be a proper mechanism for pre-campaign and post-campaign evaluation to see how much impact the campaign has made on consumer sentiments towards the brand. There are a variety of tools available to do the job.
2. Monitor consumer sentiments beyond brand mentions or likes
Various tools that enable sentiment analysis in social media and the web are Meltwater, Google Alerts, People Browser, Google Analytics, HootSuite, Tweetstats, Facebook Insights, Pagelever, Social Mention, and Hubspot’s Marketing Grader. With Marketing Grader, it is possible to find out how active you are in blogs that are available for sentiment analysis on social media, and the web. It also enables marketers to find out how the sentiments are leading to sales conversions. Facebook pages with more than 30 likes are eligible for getting insights into visitor behavior such as likes, active users, demographics, external referrals, and more.
It is important not to be swayed by the volumes of likes, brand mentions, and tweets but by whether that is generating leads, sales conversions, or a positive image of the company. Quality metrics are often not measured but ignored. They include satisfaction ratings, replies, conversations, re-tweets, and opinions, among others. Every effort involves time and cost. Therefore, it makes sense to have a proper evaluation of the efforts.
3. Sharing of sentiment analysis dataset
The objective of sentiment analysis dataset collection and analysis is not to confine it to the marketing or corporate communications department. It has to be shared with stakeholders in the organization. All business heads and unit managers need to be aware of the sentiments consumers have about the company- it will help in the formulation of strategies, plans, and policies. Moreover, the sentiment analysis dataset is actionable. If there is a negative sentiment towards product quality or service, it has to be remedied, and the first step is to make the concerned teams aware of this matter. The goal of the sentiment analysis dataset is not to confine it to a department. Still, it should be circulated to concerned stakeholders, that in turn, will help in the formulation of better policies.
4. Relying too much on automated sentiment analysis software
The problem with sentiment analysis is that for large organizations, there is so much to track on websites, social media, and other digital media. To err is human, and so are the machines or software. If a leading restaurant gets a review that is positive about food but negative about service, which sentiment would be highlighted? Experts suggest that when using sentiment analysis tools, look for one that helps you to override sentiment and toss irrelevant results. Tools that enable manual override of sentiments help in getting alerts about high-level trends, which can then be manually analyzed or monitored.
When there is a large volume of sentiment analysis datasets to be analyzed, using sentiment software would be less costly and efficient than human analysts. But experts point out that there should be an ideal mix of sentiment software analysis and manual analysis.
5. Using Keyword processing and NLP is quite reliable
Keyword processing algorithms distinguish negative and positive words, which are fast and inexpensive to implement and run. Natural Language Processing is created on the basis of the understanding of words, sentences, and phrases to get a feel for what is being communicated. Sometimes, NLP can also go wrong in language processing – how to distinguish ‘sick’ for cool or ill.
6. Using predictive analysis based on sentiments
Predictive analysis can be used to predict consumer behavior based on sentiment analysis in social media and websites. The prevailing tendency is to use article-level sentiments, but more success can be achieved with entity-level sentiments, according to leading analysts.
7. Don’t ignore the mobile
Many of the one-to-one and group conversations take place on mobile. Moreover, with the popularity of mobile apps, much of the communication happens on Android or iPhone. Several new tools have emerged that use sophisticated NLP to analyze chats, SMS, social media, and hospitality, and they are mostly cloud-based applications. Lexalytics, which launched enterprise-level NLP for Android, emphasizes the fact that all data analyzed are stored on phones and not sent to the cloud, thus ensuring privacy. The product titled Salience immediately alerts users regarding negative and positive/praiseworthy emails and messages, and a summary of such findings is given on a weekly and monthly basis.
In the modern context where mobile is achieving more penetration and universal applicability thanks to Android and Windows platforms, enterprises need to actively track mobile communications for possible clues about consumer sentiments towards their brands.
8. Beware of accurate claims
According to analysts, there are no standard measures to verify the accuracy of different sentiment analyzer tools. Hence, 70% reliability is more acceptable than 90% or above, as some work on the entity level, some on the article level, and some use NLP. In contrast, others use different algorithms to arrive at what consumers feel about your product or brand.
It is very important to go for hybrid types that can combine article-level, entity-level, directional, quotation-level, and keyword-level sentiment across content web pages, blogs, and social media. One such application is IBM’s Alchemy Sentiment Analysis.
How to Collect and Organize Social Media Data for Analysis?
Gathering social media data is the first step to start utilizing sentiment analysis. To do this, you’ll need to find relevant sources of content on sites like Twitter, Facebook, and Instagram. Once gathered, these pieces of content can be organized into categories or topics so they can easily be analyzed later on. You can also use tools such as boolean search or filters to filter out anything irrelevant or distracting from your desired results.
Preparing the Data for Analysis
The next step is to prepare the data for sentiment analysis. This involves preprocessing your content by cleaning it, removing any stop words and other common words, and parsing and tagging each piece of text with its corresponding sentiment score. A sentiment score is a numerical value that reflects how positive or negative a given comment or post is. Once all the pieces of content have been tagged with their appropriate sentiment scores, they can then be used to generate insights about trends across different platforms or topics.
Interpreting Results and Making Insights
After you run the sentiment analysis, the next step is to look at the results and see what insights you can glean from them. You can use statistical techniques to get an overview of how people feel about your brand or products. For example, if you measure sentiment scores on a scale from -1 to 1, a score that’s closer to 0 reflects a neutral sentiment. In contrast, scores closer to 1 are more likely indicators of positive sentiment, and scores closer to -1 indicate negative sentiment. With these insights in hand, you can better understand what strategies and tactics should be used to further engage with customers.
Conclusion
There is a huge demand for sentiment analysis in social media as it is capable of mining tens and thousands of documents to come up with sentiments consumers or users have of the brand or company The pitfalls of too much reliance on automated sentiment analysis have already been emphasized. Human language and writing have cultural differences, slang, and misspellings, and for machines to understand the context in which it was said or written is a daunting task. Even as experts point out the rapid improvements in automation, an adequate level of human intervention and analysis is required to make the whole process foolproof.
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This has been a guide to Sentiment Analysis in Social Media. Here we discuss the basic concept with the 8 best strategies of sentiment analysis in social media respectively. You may also look at the following articles to learn more –
