Businesses often need to analyze highly subjective data such as customer feedback, reviews, and recommendations to aid in their brand decision-making. But simply automating data analysis leads to the nuances of this data being overlooked. Sentiment analysis with Machine Learning (ML) models provides a more comprehensive solution to this problem.
Sentiment analysis refers to the training of ML models to understand text beyond face value and make sense of context, sarcasm, and interpretation. The polarity of customer's feedback can be understood; clearly defining whether their comments are positive or come with a negative connotation.
For instance, let's take the example of the following customer comment;
"I cannot thank you enough for making me opt for a completely different brand than yours. Impeccable service!"
Here, a simple ML model could read 'thank you' and 'impeccable service' as positive feedback; when in reality there is a layer of pointed sarcasm here. Training ML models through sentiment analysis can help make sense of these contexts. This can then help the business come up with a better brand repositioning strategy.
Now, let us understand how sentiment analysis really works.
How Does ML-based Sentiment Analysis Work?
Sentiment analysis is a buzzword drumming up interest from several industries. There are a number of sentiment analytics tools such as Awario, Digimind, Hootsuite Insights that use ML algorithms and Natural Language Processing (NLP). The algorithms are trained using large volumes of textual input to ultimately perceive whether the message in a sample text is positive, negative, or neutral.
An input message is broken down into phrases, evaluated by the applied algorithm, and given a score. For instance, let's take the following customer comment:
"Learned a lot from the Trailhead tool. Some chapters were slightly difficult to understand. But overall not a bad Salesforce learning experience."
The sentiment analysis algorithm would break this down into separate chunks which it would then evaluate based on a predetermined marking scheme:
- Learned a lot.... = +4
- ..difficult to understand = -2
- ...not a bad...experience = +2
While the three chunks of the text are given positive, negative, and neutral scores, the overall score for the Trailhead tool is a positive 4. So the resultant sentiment appears to lean towards a more optimistic front.
The above is a simple example where things are more boilerplate. It can be far more challenging to train a bot to understand hidden sarcasm and double entendres. Sentiment analysis is a field being constantly improved upon to find solutions to these hurdles.
To be completely sure about how and when to apply sentiment analysis, you need to know its types.
Types Of Sentiment Analysis
Different industries require the implementation of different types of sentiment analysis based on what kind of text they need to analyze. The following are the types that can be implemented based on the complexity of the respective brand’s positioning strategy:
1)Standard Sentiment Analysis
Based on the tone of the expressed opinion in the customer feedback, this type of analysis assigns values like the sample we discussed above. The resulting sentiment values are ternary in nature consisting of one of three values:
2)Granular Sentiment Analysis
This is similar to standard sentiment analysis except that the outputs are slightly more varied. The output from this type of analysis is more precise and can be pre-programmed with a few extra marking scheme additions. The range of outputs includes:
- Very positive
- Very Negative
The sentiment ranges from the extremes of 'very positive' to 'very negative' and sometimes falls in the more average marking scheme. Supervising bots analyzing customer feedback and comments are trained to recognize the sentiment behind words.
This type of analysis utilizes more complex deep learning algorithms like Long Short Term Memory Networks (LSTMs) to understand human emotions. It learns to associate textual data to different feelings like happiness, anger, and frustration based on observations.
Emotion detection suffers from the issue of lexical ambiguity associated with the difference in customers' mindsets. The same piece of text could have both positive as well as negative perceptions based on how one sees it and feels at the moment.
4)Aspect-Based Sentiment Analysis
Some customer comments and reviews refer to components or aspects of a product instead of the product in its entirety. If analyzed properly implementing deep learning algorithms, businesses and brands can gain extremely useful insights into what can be improved about a product. Specific comments about the product's lagging aspects can help eliminate issues related to the product and gain a loyal set of customers.
This type of sentiment analysis leads to proactive action-taking based on customers' gripes and grievances. The algorithm identifies the underlying intent behind a piece of textual data. The intent analysis is actually a few steps ahead of sentiment analysis and uses grammar-parsing technology.
The analysis of a customer's intention at multiple touchpoints and across platforms can help advertisers provide better-targeted ads. Each customer engagement is classified under separate categories - it could be a query, a complaint, a suggestion, or an appreciation.
Ways To Train Sentiment Analysis Models
A sentiment analysis model can be trained in several ways. Below we discuss three of the most viable ways to do so:
1)Custom Trained Supervised Model
Training a custom ML or deep learning sentiment analysis model involves the use of a labeled dataset. First, the model takes in the input dataset, conducts preprocessing of the text followed by the numerical encoding of the dataset.
The efficiency of the custom-trained supervised sentiment analysis model comes down to choosing the appropriate ML algorithm, usually the Naive Bayes classifier algorithm. Once it is chosen, the algorithm goes through phases of training and hyper tuning. After properly training the algorithm, it predicts the sentiment for the given text.
2)Word-Dictionary Based Model
In this model, large structured sets of text are converted into a dictionary of negative and positive words. The words are arranged and grouped together based on the probability of their occurrence in training datasets. A custom function analyzes textual input and assigns negative and positive values by comparing it with this dictionary.
Bidirectional Encoder Representations from Transformers, or BERT, is a highly advanced model developed by Google using NLP technology. BERT includes layers of encoders stacked on top of each other to understand the text. It uses a point-based system to make sentiment analysis predictions. The text sentiment of a wide array of datasets can be ranked from best to worst using BERT.
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Use Sentiment Analysis To Improve Your Customer-Brand Relationship
Leading organizations are leveraging sentiment analysis models to maintain a strong online brand reputation. Applying these models across multiple online platforms and social media websites allows them to analyze customer reviews and gauge the overall user sentiment towards their brand.
To pursue such innovative solutions for your brand, you need ideas that have real business value and can help build a loyal customer base. Daffodil's AI Development solutions can help you parse customer behavior data. To know how you can transform your brand image with context-aware AI-powered customer support book a free consultation today.