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Verbal communication has been the key enabler for exchanging ideas and influencing decisions among humans for ages.
The modes of verbal communication have evolved over time from mere interpersonal interaction to broadcasting ideas and opinions to the whole world, thanks to the advent of television and radio, followed by the internet and social media.
But social media in particular has drastically altered the landscape of customer interactions and engagements with brands and businesses. This, in turn, has empowered customers as they now have numerous platforms like Facebook, Twitter, blogs, forums, app stores, etc. to openly share their experiences on various products and services.
Brands can use these interactions/data points to assess customer opinions and sentiments and adapt readily to meet the changing needs of the customer. However, the evaluation and interpretation of data across these platforms pose a huge challenge to brands and businesses. This is where tools like sentiment analysis can be leveraged to gather meaningful insights from the data.
Traditionally, brands relied on structured surveys and questionnaires to gauge customer satisfaction. The Net Promoter Score (NPS) survey classified customers into promoters, passives, and detractors based on a few simple questions (e.g., “Would you recommend this company, product, and/or service to a friend or family member?”). The survey data can be easily aggregated and assessed but it does not provide additional insights on customer behavior and experience. This is where sentiment analysis comes into play.
Sentiment analysis is the process of identifying and extracting customer’s opinions and sentiments exhibited in a text. The analyzed data quantifies the customer’s sentiments and reactions toward certain products, services, or ideas, and reveals the contextual polarity of the information. Customer sentiment can range anywhere from positive, neutral or negative and no matter where customers are in the sentiment spectrum, sentiment analysis provides information on the key drivers of customer sentiments. This valuable data is a gold mine for brands and businesses as it helps them refine their products, services, brand image, and more.
Customer opinions are usually subjective expressions that describe sentiments and feelings toward a subject or a topic. Opinions can be direct (e.g., “The app does not have a user-friendly interface”) or comparative (e.g., “Support provided by Brand A is better than that of Brand B”). Opinions can also be explicit (e.g., “The product quality is very bad”) or implicit (e.g., “The product broke in two days”), which is the most difficult type of opinion to analyze.
The other complication is the way in which the words are combined in a sequence (e.g., Although “bloody” is a negative word, it may be a positive indicator if used in a phrase “bloody awesome”). Sentiment analysis overcomes these challenges and helps businesses in formulating actionable insights. It also helps in identifying recurring themes/issues (e.g., “Customer service is very slow,” “Competitor B has integrated facial recognition feature on the app,” or “Adding XYZ as one of the payment methods would be recommended,”) which can help businesses understand challenging customer experiences, competitive threats, and emerging market opportunities.
There are three ways to implement sentiment analysis systems: Rule-based systems, automated systems, and hybrid systems.
Rule-based systems
The simplest kind of sentiment analysis systems makes use of dictionary/lexicons to look at words or phrases and indicate the sentiments associated with it.
There are some popular off-the-shelf lexicons like Simply Sentiment, VADER, TextBlob, Sentiwordnet, etc. which do this job easily. There is not much training involved as this system is simple, fast, and relatively easy to use. The accuracy is decent with steady outcomes, but it does not consider how words are combined in a sequence. Hence new rules need to be added to support new expressions and vocabularies. This kind of approach works well with direct and explicit opinions. However, rules need to be customized/added for comparative opinions, and it does not work well with implicit opinions.
Automated systems
Automated systems use a mix of statistics, natural language processing (NLP), and advanced machine-learning algorithms to determine sentiments. In this technique, the models are trained to associate inputs (texts) with corresponding outputs (classifications). The machines are trained on models/classifiers with the input data that is already classified. Once trained, the model is then tested on more data and this time the model generates the predictions.
