Are You Deciphering Customer Sentiments?
Businesses are increasingly relying on sentiment analysis tools to determine their brand worth and tailor customer experiences. What aspects of banking do customers despise the most? A bank in Johannesburg was deeply worried about its value perception in the highly competitive market and leveraged Repustate’s AI-powered sentiment analysis API to derive data from two […]
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Businesses are increasingly relying on sentiment analysis tools to determine their brand worth and tailor customer experiences.
What aspects of banking do customers despise the most? A bank in Johannesburg was deeply worried about its value perception in the highly competitive market and leveraged Repustate’s AI-powered sentiment analysis API to derive data from two million social media texts that ran under their campaign hashtag. Using Named Entity Recognition, insights were fetched from the data and produced on the sentiment visualisation board. The bank measured the changes, and six months later, a similar campaign was conducted, and appropriate changes were made. Attributing to the changes made by the bank, they observed growth in customer acquisition and a reduction in their churn rate. The insights paid off, and the company did not have to second guess their decision.
Sentiment analysis, a Natural Language Processing technique (NLP), is widely used by businesses. Analysing customer feedback from online marketing surveys, product reviews, and social network conversations help them gauge their brand value and tailor their services accordingly. Experts say that there are plenty of possibilities with sentiment analysis.
Business Benefits
The number of followers and likes, survey scores, website traffic, and engagement metrics is only a part of campaign analysis. For a carefully tailored customer experience through marketing efforts, sentiment analysis can prove to be quite beneficial. It allows marketers to develop better strategies that align well with customers’ thought process in conversations about the brand and its products.
With successful campaigns comes the possibility of greater lead generation. The increased engagement and conversion from social media due to the improvements made on the products and services based on the sentiment analysis will also lead to customer satisfaction. Maintaining customer relationships gets easier when the business is looked at as a personable brand that cares about its customers’ emotions and rectifies their problems and dissatisfaction. As a result, competition wouldn’t be a concern, which would lead to increased revenue.
Experts reckon that the positive impact of sentiment analysis on a business will remain so if the analytics become a routine part of the marketing operations. After all, with changing sentiments and trends, it is important to stay two steps ahead of the competition and customer demands.
A (Data) Sorting Hat
The sentiment analysis system is not called opinion mining for no reason. Over 90 per cent of the world’s data is unstructured, and it can be overwhelming to scour through it manually. Being cost-effective, sentiment analysis can help identify customer sentiments and efficiently analyse any situation. For instance, a PR crisis on a social media platform can be sorted before a disaster awakens. Moreover, manually tagging text can be erroneous as every individual is entitled to their perspective and opinion. With a centralised sentiment analysis system, confusion can be avoided, and businesses can expect consistent and accurate insights.
The Fundamentals
While the processing can be handled by analytics, the business leaders should decide on the right algorithm depending on what the company requires. Is massive data processing the need of the hour? Or is it accuracy? Sentiment analysis algorithms can be rule-based, automatic, or hybrid. While the rule-based approach uses a set of human-crafted rules to determine the polarity or subject of an opinion, the automatic approach is a Machine Learning (ML) technique. Here, the sentiment analysis task in hand is modelled as a classification issue where the classifier is given a text, and it produces a category. Termed naïve, the rule-based approach counts the polarity metrics — the number of positive and negative words. If the positive exceeds the negative, the system signals a positive sentiment. The ML algorithm uses statistical models like Naïve Bayes, Logistic Regression, Support Vector Machines, or Neural Networks to predict the sentiment category. On the other hand, the hybrid approach combines the two techniques to provide businesses with holistic and more accurate data insights.
Mind The Language
Sentimental analysis is one of the hardest NLP tasks. Emotions and the sentiments behind words are difficult to understand even by humans. How can businesses expect a “machine” to do better? Sarcasm is one of the many language and tone indicators that is difficult to process.
Did I enjoy the campaign? Sure, sure. I was not bored at all.
Analysing content without understanding the context can be useless and only provide erroneous insights. Machines cannot read into contexts unless they are programmed with indicators to decipher them.
Additionally, emoticons in the 21st century are common sentiment references in digital data. Special character-level attention is required to figure out the right sentiment of the data set. Experts recommend preprocessing social media content and the translation of emoticons into tokens to easily be understood by algorithms.
Arabic Sentiment Analysis
In the Middle East, research on Arabic Sentiment Analysis (ASA) has existed since 2008, but it has gotten more popular in recent times due to the increased availability of online Arabic content. Being culturally diverse, companies find it difficult to keep track of the Middle East consumer conversations online. It is necessary for making informed decisions on better business developments and marketing strategies.
With Arabic being the fastest growing language in Twitter, the most utilised datasets in ASA are taken from Twitter. Yet, the utilisation of the platform is not considered sufficient anymore. The most often used Arabic sentiment analysis is the ML technique. Research indicates that diving into more Deep Learning (DL) techniques might help increase the efficiency of ASA.
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The Library
Businesses have two choices. They can either build an in-house solution or buy a tool. Open-source libraries in Python and Java programming languages are usually the best course for building a sentiment analysis solution. If time, resources, and money are not an issue, the company can immerse in the luxury of creating a personalised solution specific to their requirements.
On the other hand, SaaS tools offer pre-trained models that can be immediately integrated into the business system after a few custom modifications. Several SaaS tools include APIs for seamless business integrations. MonkeyLearn provides a model that can deliver 70-80 per cent of accurate classifications. Another possibility with the solution is to learn the workings of their six lines of code model and then train a custom sentiment analysis model.
Scikit-learn is an ML library that holds several tools for text vectorisation. The library includes implementations for Logistic Regression, Support Vector Machines, and Naïve Bayes. NLTK, an NLP library for Python, offers the possibility to train ML classifiers. SpaCy is another NLP library that provides a set of low-level functions and support for the training of text classifiers. Google also developed TensorFlow that includes a set of tools to build and train neural networks and support text vectorisation. Keras can run on top of TensorFlow and provide abstractions for multiple neural network types and provide tools for text classification. A recent DL framework, PyTorch, is being used by several brands, including Facebook, Twitter, Salesforce, and Uber. There are several online tutorials available such as TextBlob, that can help businesses analyse data for sentiment value.
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Mission in Progress
Data scientists are working hard to create accurate sentiment classifiers, but they are still years behind for a breakthrough. The latest update comes from Ping An of China Asset Management (PAAMC) in Hong Kong, which is currently focused on upgrading its NLP models to account for Chinese sentiment analysis. The company believes that the vectorisation of words is the key to the advancements of NLP algorithms.
Uber used social media monitoring and text analytics tools to figure out if their customers liked the modifications in their application. Every move they make, they find answers and confidence through sentiment analysis. Despite the challenges, experts recommend brands leverage available sentiment analysis tools and services to extract the best possible results.
