Identifying Consumer Intent: Sentiment Analysis And NLP In Social Media
The perception of emotions within textual data enables companies to understand consumer sentiment towards brands and services.
Social media has empowered clients to interact more freely than ever before with their favourite brands and share their thoughts. 80 per cent of the world 's data is estimated to be unstructured or unorganized. Every day, huge amounts of information are generated via emails, support tickets, chats, social media conversations, which form the supporting pillars of sentiment analysis.
In other words, classifiers for sentiment analysis may not be as accurate as other types of classifiers. But is the effort worth it? Sentiment analysis finds its existence in the monitoring of social media, brand monitoring, customer support and market research, offering advertisers between 70-80 percent of the time the chance to get their insights.
Understanding Customer Intent
Not only do brands accumulate a wealth of knowledge available on social media, but they search beyond the digital footprint of a consumer. Instead of concentrating on particular social media sites such as Facebook and Twitter, they also segregate the news, blogs, and forums to evaluate the feelings of the audience, not only looking at the number of mentions, but also the content of those mentions.
This helps advertisers to categorize the urgency of their online brand names automatically and warn assigned team members for responses. Sentiment analysis, a concept that combines natural language processing (NLP) and machine learning approaches, provides a sneak peek into the analysis of competitors that helps advertisers to examine their competitiveness and consider how their credibility develops over time. In addition to helping them recognise future PR emergencies, which problems need to be prioritized and urgently put out and what mentions can wait.
Sentiment Analytics can be an important part of the market analysis and customer service strategy portfolio of an organization. This important tool helps advertisers and data analysts in a much more granular way to discern consumer emotions. Looking ahead, automated sentiment analysis insights will motivate data-backed choices rather than mere intuitions, which is not always right.
Social listening is the new trend that businesses are rapidly leveraging!
By analysing KPIs such as brand recognition, brand credibility, and brand voice share, Sentiment analytics tools help assess brand health. It also involves levels of customer satisfaction and the perceptions that potential customers have about the brand. These tools compound the information and display cumulative brand sentiment in charts and graphs across a variety of inputs so that companies can track related trends without having to go through each message. It further helps to create leads, to test company KPIs and to study product decisions. Sentiment analysis advantages include: optimizing customer service, avoiding a credibility crisis, keyword tracking, and brand messaging. Basically, it helps to turn bad customer experience into a positive one.
Here are some of the Sentiment Analysis tools that we highly recommend:
· Digi mind: By defining and analysing all the related discussions between themselves and their rivals, this sentiment analytics tool lets marketers closely track their social media activity. It draws data from more than 850 million web outlets, so the company gets a holistic view of its brand opinion. In addition, this tool helps evaluate references and applies filters to customize the sentiment analysis process of a brand.
· IBM Watson Tone Analyzer: Backed by IBM Watson 's excellent AI infrastructure, Tone Analyzer explores online feelings and "tones" in the user's message, from tweets to random social media reviews. It has a specific emphasis, which is its greatest asset, on customer service and support. In order to track whether telephone agents are friendly and supportive and whether they have honestly answered the questions, Tone Analyzer reports on support conversations. Plus, what was the mood of the caller, also analysed? Have they been satisfied?
· Lexilatics: It's a solution for business intelligence that analyses various kinds of text. Lexilatics operates on NLP to process texts (breaking them into sentences to test elements such as grammar and syntax) and then conducts sentiment analysis to gauge the emotions and feelings behind the words of customers. The instrument performs categorization, theme extraction, and intention identification, in addition to sentiment analysis. This makes it easier for consumers to see the increased background and appreciate their company's relative benefits and drawbacks.
· Hootsuite Insight: All social media outlets (Facebook, Twitter, Instagram, LinkedIn), news sites, forums, and blogs are automatically analysed to uncover insights that include influencers, articles, patterns, and sentiment. The messaging feature allows team members to send private messages to each other, normally without asking social media users for a password. Hootsuite can also assign customer messages to various members of the team, and an administrator can handle these overall.
· Rosette: It was first used on social media to conduct sentiment analysis, but gradually branched out to analyse entire documents and individual entities identified in the text, such as the sentiment conveyed by consumers when referencing a particular product, business, or individual. It uses AI to perform morphological analysis, which determines a part of speech. It also features lemmatization, which groups the types of a word inflected so that data can be mined as a single term. Since it can analyse text-based information in more than 30 different languages, it is an excellent tool for foreign companies.