Customer Sentiment Analysis using Artificial Intelligence – Overview
- Sentiment analysis is the process of applying artificial intelligence to recognise the emotions expressed in text.
- It employs a predetermined metric to decide if a passage of text sounds positive, neutral, or unfavourable.
- AI can analyse millions of comments posted on social media, review websites, and online questionnaires. Data collection methods can even be applied to videos.
- Businesses can use sentiment research to detect unfavourable perceptions of their products, enabling them to take immediate action and resolve these issues.
How effective is AI-based sentiment analysis?
- In the past, companies used traditional methods like focus groups and surveys to determine how customers felt about their products.
- Businesses can mine vast amounts of data, including that from social media, by using big data analytics to get a more precise picture of customer opinions. Currently, a subfield of natural language processing is called sentiment analysis (NLP).
- The basis of sentiment analysis is one of these two techniques.
Sentiment analysis based on rules:
- The rules can recognise words using NLP techniques like tokenization and stemming before looking up terms in databases. The final emotion score is paired with additional rules that take into account negations, dependencies, and other issues in rule-based approaches.
- Because it ignores word order and is unable to discern between irony and humour, rule-based sentiment analysis is naive. You can always introduce more regulations, but doing so will modify the ones that are already in place. This plan requires ongoing maintenance and adjustments.
Sentiment analysis based on machine learning:
- We train an ML model to extract information using labelled datasets. After sufficient practise, the algorithm will be able to infer sentiment from brand-new messages. In addition to predefined criteria, it may learn to recognise sarcasm, synonyms, and other challenging situations.
- Implementing a hybrid system that combines rule-based and ML methods is an additional choice. Numerous articles claim that this approach frequently yields more accurate results.
Benefits of sentiment analysis
Enterprises can achieve the following goals due to sentiment analysis, which turns unstructured data into informative data:
- Keep your current clients while bringing in new ones.
- You may improve the effectiveness of your marketing activities by being aware of how your customers are interacting with them.
- Prioritize customer service issues. Utilizing AI-based sentiment analysis, you can rearrange customer service cases in the queue to quickly address unfavourable remarks.
- Recognize consumer perceptions of the merchandise. Businesses determine which attributes are most important to customers and what may be improved.
- Keep tabs on how a product’s changes effect user views. For instance, you can watch how customers respond when new features or a product’s user interface are added.
- You should anticipate consumer attrition. Businesses may monitor heated online discussions in real-time and take action when a dissatisfied customer is about to leave by using sentiment analysis.
Sentiment analysis in telecommunications:
- Finding out why a consumer is calling a call centre is one of the most challenging duties for either a representative or a company.
- The majority of the time, call centre agents are unable to determine the customer’s mood and end up serving or responding to them improperly.
- Customers’ complaints about the services and goods rise as a result of this.The decline in brand value is due to the difficulty most businesses have in determining customers’ moods and offering solutions accordingly.
- The major drawback of being unable to determine the customer’s sentiment may now be overcame by call centres or any company providing customer support or services thanks to AI.
- This AI model analyses a large volume of calls using a Speech-to-Text model and separates call feedback.
How we developed a sentiment analysis using AI for Call Centre company:
- We have developed a model that can understand the mood of the customer and provide appropriate services for the customers.
- Conversion of call audio to text format (Speech to text conversion).
- Pre-processing of Audio text data.
- Feature selection for selecting the most important features from call.
- Building a classification model to predict whether the customer in the call is further serviceable or not.
Market size: Sentiment analysis
The global sentiment analytics market was valued at USD 3.15 billion in 2021 and is expected to grow at a CAGR of 14.4% during the forecast period.