One-third of customers say they will stop doing business with brands they love after just one bad experience. Thus, understanding how customers feel about products or services is crucial for business success. Companies use sentiment analysis methods to understand customers’ sentiments and improve their products and services accordingly.
Sentiment analysis is a Natural Language Processing (NLP) method that helps identify the emotions in text. By categorizing sentiments in social media posts, surveys, or reviews, companies can measure how their strategies work and determine new ones for growth. There are several methods to conduct sentiment analysis, each with its strengths and weaknesses.
Here, we provide an overview of sentiment analysis methods and the advantages and disadvantages of each.
Source: Artificial Intelligence Review
Figure 1. An overview of the most frequently used sentiment classification techniques
1. Lexicon-based Methods
Lexicon-based, also known as knowledge-based approaches
, are pre-developed manually and refer to analyzing semantic and syntactic (i.e., patterns in grammatical syntax) patterns. While the former refers to generating a dictionary by tagging words, the latter involves the consideration of syntactic patterns.
Dictionary methods generate a dictionary by tagging words, and corpus-based methods involve the consideration of syntactic patterns. The sentiment score of a text is determined by the following:
- Give each token a separate score based on the emotional tone
- Calculate the overall polarity of the sentence
- Aggregate overall polarity scores of all sentences in the text.
- Lexicon-based sentiment analysis methods are easily accessible as many publicly available resources (e.g., SentiWordNet) exist.
- They are less expensive because they do not require implementing advanced sentiment analysis algorithms.
- There is no need for training data, especially if companies use a dictionary-based approach, as the tags are determined manually, and there is quick access to the meaning of the words.
- Lexicon-based sentiment analysis methods usually do not identify sarcasm, negation, grammar mistakes, misspellings, or irony. Thus, it may not be suitable for analyzing data gathered from social media platforms.
- As the whole classification is based on tags and rules, companies should have sufficient data to create a reliable dictionary.
- They are very strict and domain-dependent in that a word is labeled as the same no matter the context. For instance, the term “amazing” can be either positive or negative, depending on the context.
- They are prone to human bias. For instance, if the people preparing the dictionary don’t have sufficient domain knowledge, the method won’t yield accurate results.
- As the labeling is handled manually, data preparation can be time-consuming.
Feel free to check our article on the top 5 sentiment analysis challenges and solutions.
2. Automated/Machine Learning Methods
Automated sentiment analysis methods include ML algorithms that categorize sentiment based on statistical models. The sentences must be transformed into vector space to implement machine learning algorithms. Then the models can be trained to predict the sentiment of a sentence.
Machine learning algorithms:
- Can be trained to detect sarcasm, irony, or negation in sentiment analysis. This can ease social media sentiment analysis.
- Learn the affective valence of the words, so they do not require a pre-determined dataset.
- Are faster than traditional sentiment analysis methods.
- Provide more accurate results.
- Companies need a large or high-quality small dataset to have accurate classifications
- Noise (e.g., emojis, slang, or punctuation marks) can reduce accuracy
- Costs are higher compared to traditional, rule-based methods.
Figure 2. A comparison of automated and lexicon-based sentiment analysis methods
Check our comprehensive article to learn more about crowdsourcing sentiment analysis and how it differentiates from traditional or automated methods.
Machine learning algorithms used in sentiment analysis include:
It is a supervised, probabilistic classification approach based on Bayes’ Theorem and is used for feature extraction. This approach assumes that each token or feature is independent of the other so it can handle any misspellings or grammar mistakes.
Support Vector Machine
It is a non-probabilistic classification technique that determines the best hyperplane between different vectors, allowing for strict boundaries between categories using maximized margin distances.
Figure 3. Visualization of how a support vector machine works
Word Embedding (word2vec)
It is a method that considers the frequent context that the words used. The vectors are learned similarly to the neural networks, so word embedding is considered a deep learning method.
First, the number of dimensions is determined, and each word’s position of vector values is represented in the space. While the words used frequently are closer to each other, the words rarely used together have a long distance between them. Word embedding models have the potential to provide state-of-the-art results, yet due to their resource-demanding mechanism, they are more challenging and expensive than the other methods.
3. Hybrid approaches
Both lexicon-based and automated methods have advantages and disadvantages. Thus, companies can implement hybrid methods that include automated and lexicon-based methods so that different approaches can compensate for each other’s flaws. The combination can either be parallel or at different stages of the analysis.
Source: Applied Intelligence
Figure 4. An example of hybrid sentiment classification
Check our data-driven list of sentiment analysis services to find out which option satisfies your company’s needs.
- Top 4 Real-Life Examples of Sentiment Analysis
- Top 5 Open Source Sentiment Analysis Tools
- Challenges and Methods for Multilingual Sentiment Analysis
Feel free to contact us if you have questions regarding sentiment analysis:
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