A wise old owl lived in an oak.

The more he saw, the less he spoke.

The less he spoke, the more he heard.

We are aware that active listening builds stronger interpersonal relationships. It stands valid in forming a stronger connection with a potential audience and a loyal customer base over a period. 80% of today’s data is unstructured, in the form of reviews, tweets, and comments on various social media platforms making social listening an effective way of capturing customers’ sentiments towards a brand, organization, or person. Sentiment analysis is the contextual mining of words to understand the social sentiments of customers using natural language processing (NLP) algorithms, statistics, and text analysis. It focuses not only on polarity (positive, negative & neutral) but also on emotions (happy, sad, angry, etc. This methodology mines humongous piles of unstructured data cost-effectively. It classifies them to track any issue with a product or brand in real-time, leading to quicker solutions.

There are three approaches used:

1. Rule-based system: This methodology includes sentiments lexicon (dictionary of pre-labeled words or expressions), tokenization, and parsing. This approach counts the number of positive and negative words in the given dataset. If the number of positive words is greater than negative words, then the sentiment is positive, else vice-versa.

For instance, if a review on a dress says, “The dress looks okay, but the fitting is horrible.”

The first step is to look for words from the dictionary in the text it’s analyzing (entity extraction). The text has two words from the data dictionary, and the machine learning algorithm will add up the scores and get the average score based on the total number of words matched.

(0+(-1))/2=(-1)/ 2= -0.5

Finally, based on the score and the pre-determined boundary of results, the review will be rated as positive, negative, or neutral. The sentiment score is below zero, so this review is categorized as ‘negative.’

Pros: Rule-based systems are easier to understand and deploy than the ML technique. Based on the target audience and industry, the business has close control over the vocab list and can easily update it for better coverage.

Cons: Rule-based systems break the entire sentence to match individual words or phrases, which may not fully comprehend the context or sarcasm. Moreover, since there is a finite set of words in the list, which may not allow NLP to understand the context in a dynamic environment, in a nutshell, the lexicon-based technique of sentiment analysis requires fine-tuning.

2. Automatic Approach/ Supervised Machine Learning: As the name suggests, this approach works with ML  algorithms where trained datasets are fed to ML algorithms enabling them to make predictions. The prepared data sets contain the document whose sentiment has already been determined by human evaluators (data scientists). The computer then learns the sentiment analysis classifiers of the document from the training set and labels new input data (the test set).

In the next step, words are extracted from the text for analysis. This text extraction can be done using different techniques such as Naive Bayes, Linear Regression, Support Vector, and Deep Learning.

 

The system assigns a score to all topics, categories, words, and phrases in the text based on the analysis. For instance, (-1 )to ‘extremely negative’ and (1) to ‘extremely positive’. The score of each analyzed word is added and divided by the total number of words analyzed to get the average score.   The average score reflects the overall sentiment of the text, depending on the criteria set for each sentiment category.

For instance: Below 0 Negative, 0-.5 Neutral, .5-1- Positive

Pros: Supervised machine learning techniques allow for creating trained models tailored for the specific purpose of the data analysis.

Cons: These models will often have poor adaptability between domains or different writing styles.

3. Hybrid Approach: Both approaches (Rule-based approach & Automated approach ) are combined for wider coverage and accuracy in the Hybrid approach. Typically faster than the ML algorithm, the rule-based technique may not create certain sentiment predictions. Consider a scenario where the sentence content under review has few to no words in common with the available vocab list. Here, the machine learning algorithm is used to identify the sentiment.

Pros: Hybrid approach brings along the best of both techniques, providing better insights for marketers and businesses. Businesses can have the performance benefits of lexicon-based techniques(faster analysis) but surpass their inaccuracy to account for statements whose sentiment can’t be easily identified with a rule-based approach.

Cons: Hybrid models demand a high investment of time, effort, and money.

How does social listening benefits businesses?

 Understanding reasons for spikes: Social listening is beyond categorizing public sentiments into positive and negative. It monitors spikes in mentions in real-time, giving a deeper insight into campaign management. It ensures early detection and prevention of negative mentions. Digging deeper into historical and real-time data can also help understand what caused the spike in negative and positive mentions.

Benchmarking Performance: In closely competitive industries like FMCG, automotive, retail, and others, keeping an eye on competitors not only helps to understand the industry trends but also lets a brand benchmark its own KPIs.  Monitoring your competitors’ sentiment will help you see which aspects of their products customers are most or least performing and how the brand can tap the untapped audience.

For instance, Jeep has 45 % positive sentiments here, and Dodge has 17 % positive mentions. This clarifies another competing brand on what number they may target for positive mentions to compete.

Challenges of Sentiment Analysis:

For ironies and sarcasm, memes, people use positive words for negative emotions, which is hard for NLP to break down the text and understand the right sentiment.

Sometimes, a particular mention becomes positive or negative, depending upon the context. Analyzing sentiment without context would hardly help to understand the exact sentiment expressed in any piece of text. Unlike humans, machines cannot detect or interpret context unless mentioned explicitly.

For example, WOW! It took me only 20 mins to get a regular coffee from Dunkin today!

 

• Can you think of a brand that aces its social media game, brand management, or customer service because they are an active social listener?

Citations:

1. What is Sentiment Analysis? A Definitive Guide. (2022. What Is Sentiment Analysis? A Definitive Guide. https://www.bytesview.com/blog/what-is-sentiment-analysis/
2. 5 Sentiment Analysis Examples in Business. (2021. 5 Sentiment Analysis Examples in Business. https://monkeylearn.com/blog/sentiment-analysis-examples/
3. Understanding Sentiment Analysis in Social Media Monitoring. (2021. Understanding Sentiment Analysis in Social Media Monitoring. https://unamo.com/blog/social/sentiment-analysis-social-media-monitoring