Building a Sentiment Analysis Model: Manage Your Reputation

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Successful businesses use their customers’ endorsements to build credibility and increase revenue. However, customers’ sentiment is just as crucial as any review scores a company may receive.

Natural Language Processing (NLP) aims to program computers to process and analyze vast quantities of unstructured text written in natural languages. In NLP data labeling, it is essential to consider the degree of agreement among observers on the same source data. On that note, the inter-annotator agreement metric Krippendorff’s Alpha is becoming increasingly popular with NLP data labeling due to its various advantages over other statistics.

Sentiment analysis is among the most critical areas of NLP data labeling. This type of analysis is the extraction of significant patterns from text data. The public’s perspective can be ascertained through sentiment analysis, allowing business owners to glean helpful context information.

Automated Systems

Automated sentiment analysis methods rely on machine learning models such as clustering. The classifier receives lengthy texts and categorizes them as negative, neutral, or positive.

Rule-Based Systems

Rule-based methods rely on human-created rules to sort data. Tokenization, parsing, and stemming are some of the commonly used methods.

Customization is an advantageous feature of rule-based systems. These algorithms can be adapted to fit different scenarios by creating more nuanced rules.

Hybrid Systems

The most sophisticated, effective, and extensively used method for sentiment analysis is hybrid processes. Hybrid models combine the effectiveness of machine learning with the adaptability of personalization.

Techniques for Developing a Sentiment Analyzer Using Machine Learning

There are several ways to build or hone a sentiment analysis model. This article will review five techniques.

1. Custom Trained Supervised Model

It’s possible to train a customized machine learning or deep learning model for sentiment analysis. To successfully train an ML model, a labeled dataset is essential. The ML model will pick up on a wide range of patterns within the dataset, allowing it to make inferences about the sentiment of hidden text.

For someone to train a unique sentiment analysis model, they must perform the following actions:

  • Gather unprocessed, labeled data for sentiment analysis
  • Textual Preprocessing
  • Numerical text encoding algorithm selection in machine learning
  • ML model hyper tuning and training
  • Prediction

If the number of observations is not distributed evenly, the classification accuracy may be deceiving. For a clearer picture of the accuracy of predictions, look at the corresponding confusion matrix.

2. TextBlob

TextBlob is a freely available Python library for processing textual data. Its straightforward API lets you choose which algorithms to employ behind the sequences. The API may be utilized for various purposes, including categorization and sentiment analysis. The library offers two configurations for sentiment analysis, which have been used in modern NLP data labeling.

3. Word-Dictionary-Based Model

The text corpus generates a positive and negative word n-gram dictionary. The technique necessitates a labeled data set and uses specialized python functions to create an n-gram dictionary.

Additionally, domain-specific words can be incorporated into the dictionary to provide additional benefits.

After compiling a dictionary of positive and negative words, you can use that with a specially made input text. The value obtained increases for every upbeat word in the input text and decreases for each downbeat word.

To standardize the final sentiment score, divide it by the total number of words in the text function to determine whether a given piece of text conveys a positive or negative sentiment.

A score of 1 indicates a 100% confident prediction of positive sentiment, while a score of 0 indicates nothing. A negative sentiment score, on the other hand, can be anywhere from -1 to 0, with -1 indicating a certain prediction of negativity.

4. Named Entity-Based Sentiment Analyzer

The primary focus of the named entity-based sentiment analyzer is entity or relevance text. Target sentiment analysis, which centers only on the most important words or entities, is more precise and helpful than the three approaches mentioned earlier.

  • The initial step is to identify all named entities within the given text.
  • Implement name entity recognition to the text to identify a variety of entities, including PERSON, ORG, and GPE.
  • Insights into public opinion based on the most frequently mentioned names.
  • Targeted by isolating and analyzing only the sentences that explicitly mention the key phrases, one at a time.


Google’s Bidirectional Encoder Representations from Transformers (BERT) is a cutting-edge machine learning model for natural language processing. Here are the steps one should take to train a BERT-based sentiment analysis model:

  • Configure the transformers library
  • Load the BERT classification algorithm and tokenizer
  • Build a processed dataset
  • Set up and train the loaded BERT model while refining its input variables
  • Make sentiment analysis-based forecasts

Final Thoughts

Many businesses use sentiment analysis to comprehend customers’ perceptions through reviews and social media conversations to make more rapid and reliable strategic decisions.

The article has highlighted five distinct methods of developing a sentiment analysis model. However, it’s important to note that no single method can be used as an ironclad guideline when creating a sentiment analysis model. It requires thinking ahead and adjusting the algorithms to the problem description and data set.

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