Sentiment analysis data sources give you the necessary tools that are needed to gain a deep understanding of market sentiment and thus help you influence market behavior. Sentiment analysis of data from customer feedback, brand performance, employee engagement, market trends, and such, also enables you to make smarter, faster decisions for growth.
In this blog, we read about 10 such sources of sentiment analysis data that can be strategic in shaping your machine learning-driven business strategy.
What Is Sentiment Analysis And Why Is It Important?
Insights from sentiment analysis data sources help a business identify how customers feel about its products and services. Sentiment analysis NLP (natural language processing) of this data is vital for formulating necessary corporate and marketing strategies. This applies to all departments of an organization, especially product marketing, human resources, as well as data scientists.
Furthermore, organizations need to analyze sentiment analysis data to a greater extent today because of the dynamic nature of digital and social marketing channels, giving rise to cut-throat competition. Done right, sentiment analysis can be crucial to a company in the following ways-
- Business intelligence
- Capturing new markets
- Product/service gap analysis
- Better sales conversions
- Competitor analysis
- Employee engagement
- Increased brand value and visibility
- Strategic marketing propositions
- Product differentiation
- Enriched customer loyalty
What Are The Sources Of Gathering Sentiment Analysis Data?
There are ten main sources from which relevant sentiment analysis data can be gathered for analyzing sentiment. These include news, public information, social media, customer reviews, customer service call center data, employee interaction data, electronic health records, and more.
Let’s review them in detail.
1. Social Media
Sentiment analysis data sources from social media include platforms such as TikTok, YouTube, Instagram Live, Twitter, Tumblr, WeChat, Reddit, Youku, and others. Interestingly, you can extract relevant information from not only comments but the videos themselves through social media sentiment analysis.
2. Customer Reviews
Sentiment analysis data for customer experience such as consumer comments and reviews can be gathered from review websites such as Google reviews, Superpages, Demandforce, Clutch, etc. You can further narrow down your data by targeting websites specific to an industry such as Expedia, Travelocity, TripAdvisor, and such for travel and hospitality; Yelp, Yahoo! Local, etc for restaurants and local businesses; or even websites like Indeed and Glassdoor for employee reviews.
3. News articles and videos
Articles and videos from news websites are an apt source of sentiment analysis data for various business needs, especially brand reputation monitoring. For example, stock market news is perhaps the most highly anticipated source of information in an industry where political unrest, financial reports, oil market reports, all have a significant impact. A company can compile all relevant videos in its repository and arrange and analyze them with the help of video AI that includes features like search inside video and semantic classification.
4. Public Information
Information from public sources such as business journals, trade publications, government departments, social media groups, etc, which don’t need third-party permissions are excellent sentiment analysis data sources for strategic branding and market research initiatives.
5. Employee Interaction Data
Employee feedback can be a vital source of information for employee experience (EX) analytics for improved corporate work culture and employee satisfaction. Data can be gathered through audio platforms, chatbots, videos, HR interactions, intranet chat, employee surveys, and other voice of the employee methods.
6. Electronic Medical Records
Electronic medical records (EMR) hold all the information about a patient - from vital statistics, to medicine dosage and reactions, to their personal information such as age and address. Thus EMRs are a good source of collecting information for healthcare data analytics. Additionally, sources of patient voice reviews on websites such as SeniorAdvisor, RateMDs, RealSelf, Healthgrades, and the like are great as well.
7. Customer Service Support Logs
Customer service call center support logs are a great source of data for customer experience sentiment analysis. Customer service emails and chat interactions can give key information as to what customers like about a company’s products and services, and what they are unhappy about. So do customer support calls. In fact, it is for the very purpose of voice of the customer analysis that these calls are recorded.
Would you be concerned about what people are saying about you?
Sentiment analysis is essentially the process of aggregating, understanding, and analyzing the views of people about a particular brand.
The term is also known as mining opinions in data analytics lingo.Sentiment analysis as a process helps the businesses in understanding whether the underlying emotion about their brand is neutral, positive, or negative.We are producing 2.5 quintillion bytes of data daily.
The insights gained through sentiment analysis can enable your brand to create positive sentiment about your brand by designing public relations tools.Benefits of Sentiment AnalysisOptimize your marketing strategyConducting functional sentimental analysis helps brands in optimizing their marketing strategy as they can know if their marketing strategy is proceeding in the correct direction or not.Evaluating the ROI of your marketing campaignSentiment analysis helps in evaluating the ROI of your marketing campaign.
Thus giving you ample time to respond to these negative discussions as the earlier you react, the better it is.By adjusting your marketing campaign according to the preferences of your customers, by improving the product quality substantially and by extending excellent customer support, you will increase the chances of gaining better leads.As they say, in business, perception is more important than the actual thing.
When Nike announced that it was taking the controversial NFL quarterback in its latest announcement, the customer sentiment was overwhelmingly negative.
Investors weighed signs of the economy, recovering against a rise in coronavirus infections in the global Sentiment Analytics Market.
Market Research Future digs into account and found that the market would rise at a pace of 14% CAGR and would reach USD 6 Billion valuations by 2023.
The study reveals that the overall market would get bigger post the pandemic at an increased pace with the invention of the new-fangled dynamism, which is making rapid progress in the market’s growth, mainly from the years 2017-2023 (forecasted period).
Request a Free Sample @ https://www.marketresearchfuture.com/sample_request/4304 Competitive Outlook:The well-known players in the sentiment analytics market are included as Clarabridge (U.S.), Angoss Sentiment Corporation (Canada), SAS Institute (U.S.), IBM (U.S.), Bottlenose.Com (U.S.), Aylien (Ireland), Brandwatch (U.K.), Crowdflower (U.S.), Adoreboard (U.K.) and Twizoo (U.K.).Segmentation:The sentiment analytics market is further segmented among the segments, such as components, deployment, organization size, and vertical.Depending on the organization size segment, small and medium enterprises hold the largest market share.
Escalating competition creates an opportunity for businesses to collect efficient data related to sentiment development is one of the prime factors boosting the sentiment analytics market.Sentiment analytics market by components segment: Comprises sentiment, professional services, service and support, and maintenance services.Sentiment analytics market by deployment segment: Comprises cloud and on-premise.Sentiment analytics market by vertical segment: Comprises BFSI, transportation & logistics, retail, education, healthcare & life sciences, media & entertainment, and others.
Regional Analysis:The regional analysis of the sentiment analytics market is being studied for regions of the Americas, Europe, Asia Pacific, and the Rest of the World.
