Sentiment Analysis Of Comments on Social Media
Social media comments analysis is the process of understanding the sentiments expressed in social media feeds to see how they affect a brand. Through sentiment analysis of comments, companies can use social media-based marketing and advertising to creatively build their brand spotlight. They can confidently leverage insights from a sentiment analysis API for data-backed holistic growth strategies that are customer-centric and compounding.
Why Do We Need Social Media Comments Analysis?
Any company that launches a new product, advertising campaign, brand ambassador, store location, or simply wants to know where it stands in the current market space compared to its competitors, needs to know how sentiment analysis of comments on social media is done. This gives it a heads up as to what to expect from a good social media PR standpoint, and also to see what it can do to improve itself.
In the current scheme of things where people have access to social media readily, not only are business-to-customer (B2C) companies leveraging the channel but also B2B companies that were once averse to it. Take, for example, Intel, with their highly successful Sponsors of Tomorrow campaign. The company realized that even though they were not directly affected by consumers on social media, social media-driven brand awareness is an advantage as an overall business strategy.
Overall, social media comments analysis can help businesses with:
- Brand intelligence
- Improving customer experience
- Better sales conversions
- Brand awareness
- Brand loyalty
- Competitor awareness
- Product enhancements; and more
Read more about benefits of social listening.
How Is Sentiment Analysis Of Comments Done?
A machine learning-based voice of customer analysis platform performs sentiment analysis of comments in 4 stages.
Stage 1: Gathering comments from social media
Once you have decided the social media channels you want to analyze, it is time for the first step in the social media comments analysis process. In this stage, data is gathered in one of two ways, or both, if necessary. It is then annotated. You can gather data either through APIs or upload it manually.
- Data gathering through APIs - Social media channels usually have APIs through which you can download publically available data as with the Facebook API or Twitter
- Data gathering manually - The sentiment analysis platform also allows you to manually upload data as a .csv file
Stage 2: Processing the comments
Now that all the data is collected, cleaned, and labelled, it will be processed with several machine learning tasks so that all formats of data present in the comments are prepped for sentiment analysis. This is important because people often respond in comments with memes, gifs, images, and emojis. The ML algorithm recognizes the text in all these formats and includes them with the text from comments. It is now ready to analyze this refined data to extract entities, aspects, and topics from it.
Stage 3: Analysing the data
Now that all the data has been processed, the model needs to be trained so that AI tasks like natural language processing, named entity recognition, semantic analysis, topic classifications, and sentiment analysis of comments can advance.
- ML model training - A dataset pertaining to the industry the brand is from, is used to train the model. Another data set is used to validate the results and compared with the correctly classified data. The model is trained multiple times to ensure that the results are as accurate as can be.
- NLP for multilingual data - Natural language processing (NLP) tasks analyze data in each language separately. In the case of Repustate, the social media sentiment analysis platform analyzes data in 23 languages natively without translations. It uses a speech tagger for each language individually.
- Tags - Tags can be auto-generated or created manually for aspects, themes, and topics identified in the data. Thus, when insights are needed, for example, TikTok insights, custom tags will identify brand mentions, celebrity names, product names, etc.
- Topic classification - The topic classifier attaches a theme to a text, like price, healthcare, education, food, etc.
- Sentiment analysis - All the topics, aspects, and features are analyzed for sentiments that may have been expressed for them and sentiment scores between -1 and +1 are assigned. Eventually, all these sentiment scores are aggregated and the subject matter is assigned a positive or negative score anywhere between 1 and 100. 1 being the most negative and 100 being the most positive.
Stage 4: Visualizing the social insights
The insights gained from stage 3 are now visualized on a central text analytics dashboard in the form of actionable reports. You can see emotions for all aspects, and even the coinciding comments which are colour-coded - green for positive and red for negative. Alerts can be set based on mentions or keywords, and you will be notified via email or phone messages so that you can respond to them in a timely manner. Through charts and graphs shown in the visualization dashboard, you can know which aspects of your business are popular amongst customers, and which ones are in need of improvement.
What Are Some Major Comments Analysis Tools?
Here are the top comments analysis tools:
Repustate IQ can analyze comments in 23 languages without translations across a range of social media channels. AI-driven NLP, sentiment analysis, data mining, named entity recognition, and other key data analysis techniques mean that you get the most accurate insights from its social media mining capabilities. You can use it as an API or go for the complete solution that comprises a comprehensive visualization dashboard
Sprout Social conducts impeccable social listening too that also allows you to respond to social mentions. Like Repustate IQ, it allows you to filter your insights by keywords and hashtags and refine your search so you get targeted insights. The platform is apt for large enterprises and has many features that allow it to integrate into your CRM tool.
This comments analysis tool allows you insightful discoveries from customer chatter. You can filter your data by keywords, hashtags, locations, and language. Mostly used for Twitter, it not only allows you to share messages but also take actions like retweet, reply, or respond in a private mode.
Sprinklr is really good if you are a large organization and want to integrate it into your brand awareness campaigns. It has a user-friendly visual dashboard, where you can analyze the insights from both historic as well as real-time data. It allows you to analyze vast data but with some coding required, unlike Repustate IQ. It also has a range of templates that you can use for your insights to share with other teams.
Meltwater is a comments analysis tool that you can use to gather customer and brand insights, as well as monitor your social media performance, perform search queries, and track areas that need your attention. Widely used by journalists, the platform has many other features such as media monitoring and newsletter creation.
A tool for comments analysis, Synthesio is also multilingual and allows you to track views, monitor replies and check retweets on Twitter. You can also analyze posts on Facebook, Instagram, and other channels. You can export pre-filtered data sets through the API and measure your social listening performance with quantitative data.
BrandMentions enables you to monitor social media platforms for brand mentions and is apt for brand experience analysis. The tool helps you assess customer experience through social chatter and provides you with alerts and notifications so you can respond to negative comments or analyze trends in hashtags.
YouScan lets you browse through social media conversations and filter data according to themes, recurring topics, and hashtags. Like other tools, you can get notifications so you can take quick action when it comes to your brand mentions. You can access several social media platforms and see the insights on a user-friendly dashboard.
Avario is good for small-size companies that are new to ML-based social media listening. It can access and analyze data from public data on social media. Here too, you can track brand mentions, analyze quite a few social media channels, and get timely insights.
This comments analysis tool allows you to track social media conversations and is also pretty simple to use. It has a useful dashboard where you can see your insights. It also helps you in coordinating your social media engagement by allowing you to post content directly from the dashboard and engage with customers.
A cutting-edge social media comments analysis tool like Repustate IQ can supercharge your marketing strategy by harnessing the treasure trove of information on social media by customers and prospects themselves. It can automate data gathering and provide you with granular social media sentiment analysis so you can have in-depth insights into every aspect of your business that is available to customers.
Sentiment analysis of comments has shown that people of all ages feel that social media has for the most part positively affected them thanks to free access to information, community groups, art, and self-expression. This proves how important social media is and how important social media comments analysis can be to a brand. And more importantly, why every organization needs to leverage a comments analysis tool meant specially for social media listening.