How To Implement Aspect-Based Sentiment Analysis with Python
Aspect-based sentiment analysis is a text analysis method that categorizes data by aspect and identifies the sentiment attributed to each aspect. It is commonly used for a variety of tasks, including better understanding a speaker’s thoughts within content. It’s quite exciting, and you can use it.
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Aspect-based sentiment analysis with Python is one route to using it. To do so, you’ll need to understand what it is and how to handle data processing, including text cleaning and using one of several aspect term extraction methods. In this guide, we will explore the business applications and what is required to be proficient before providing you with some how-to steps to use.
Understanding Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is sometimes referred to as fine-grained opinion mining. It is the process of determining the sentiment of the data as it applies to the specific aspect. This is a cutting-edge, new field that is growing at incredible rates thanks to the explosion of AI. The reason ABSA is so important? Traditional sentiment analysis is not enough.
Traditional sentiment analysis treats all of the data as a whole. It assists a single sentiment label to it, such as “neutral” or “negative.” Sometimes, this works just fine. Other times, it is helpful to know the sentiment of a text based on the specific aspect.
Here is an example. A person purchases a product and gives it an average rating. However, the purchaser notes that while it was an average product, they noted the customer service of the retailer was poor. In this way, it is necessary to see the difference between the two components.
How Can Aspect-Based Sentiment Help You?
A variety of use cases exist for ABSA applications. When we consider aspect-based sentiment analysis, the goal is to consider when it is necessary to have various insights from the same data. To help you determine that, consider the following use cases.
Customer satisfaction: One of the most effective benefits of aspect sentiment analysis is the use of monitoring specific product or service satisfaction levels. This allows the tracking of customer reviews or comments about the product online. In this way, it is possible to better understand what a customer is saying about the product or service in a meaningful way, allowing for actionable changes.
Public opinion: Many companies know the value of their reputation, but they also know that it is not uncommon for a simple conversation online to change public opinion of their brand. Using ABSA, you can track not only the mentions of your brand but also the opinions better, allowing you to make changes or address concerns.
Utilizing aspect-based sentiment analysis, you can gather a far richer understanding of what people mean and not just what they say.
Types of Aspect-Based Sentiment Analysis Dataset
Let’s say you want to understand how well a product is perceived by customers. You want to analyze feedback provided by associating the specific sentiments with various aspects of that product.
There are two specific categories of aspect-based sentiment to consider:
- Supervised ABSA: In this method, you will provide the seed words to help the supervised model extract aspects from the sentence. When it identifies the particular aspects of that sentiment, it will tag those specific sentences for reflection.
- Unsupervised ABSA: It is not always possible to provide a seed for each sentence, especially when you have a large dataset to navigate. One way around this is through the use of topic modeling, a method that extracts latent topics from the data. An example of this is the latent Dirichlet algorithm.
Aspect Sentiment Analysis: Training an ABSA Model
One of the more complex aspects of aspect-based sentiment analysis is that you must be able to train the ABSA model to identify “aspects” within the data and assign sentiment labels to those specific aspects. The most common way to do this is to identify aspects in the text and then use one of the ABSA models to label the sentiment of each aspect.
Rule-Based Method: This is one of the most common approaches as it operates like a dictionary. For example, if you are looking for Product ABC, any time “Product ABC” is listed in the dataset, that can be considered an aspect.
That is the first step, but it requires you to train an ABSA classifier, which works to classify the sentiment of the aspect as it relates to the sentence itself. Not only do you want to find mentions of “Product ABC” but you also want to know how the customer felt about it. If they did not like one of the features or had a delay in shipping, you need to know those details, not just the overall experience.
As noted above, supervised machine learning is one of the most common methods for aspect-based sentiment analysis. In this situation, you need a training dataset, which is a grouping of texts that have been labeled with aspects as well as their sentiment. You can find data sets to use online that could help you handle the breakdown of this process a bit easier than trying to create your own.
Consider the Importance of Web Scraping in Aspect Sentiment Analysis
If you want to gather all of the product reviews for Product ABC, you need to find a way to locate those words across the huge range of websites that could be talking about it. You could work through this process and capture data yourself, or you can use web scraping to help you get the data you need far sooner.
Web scraping is the method that enables you to extract a large amount of data from the websites you select. This is done for you through an automated process, alleviating the time and work you have to put into the process yourself. Because much of the data you are collecting is unstructured HTML data that you will then convert into structured data using a database, web scraping can facilitate the process quickly and easily.
Web scraping: To show you how to use web scraping to capture the data you need, take a few minutes to learn more about Python web scraping (and the benefits of using a proxy with it). You may know the value of using web scraping, a process of capturing data from specific websites that you can later analyze. A proxy service helps you avoid being spotted or limited by a website that does not want you to capture that information. With rotating data center proxies, for example, you are fully protected from being discovered or your own personal information captured.
Let’s assume you want to use a web scraping method. Doing so with Python’s Beautiful Soup library makes the entire thing a bit easier to navigate. Take a few minutes to consider what makes this one of the best tools for web scraping. You can build your web scraper to capture the specific information you need after using those tools.
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Removing Missing Data: Once you do this, you will have a good amount of data available to start using. However, it will be necessary to navigate any missing values. It is rather common to have missing data in real data sets. When even one data point is missing, that can be problematic. This can happen for various reasons, such as random events, merging of source data sets, or failures of measurement.
Clean the Text: The next step is to engage in text cleaning. This is the process of extracting raw text from input data. You will convert that rare text to the required encoding format by removing all of the non-textual material. That includes all of the elements, such as markups and metadata, that are not providing you with the information you need. Text cleaning is important and should not be ignored since it can influence the final outcome of your project.
Eliminate URLs: Another concern within raw text is the presence of URLs, which can confuse national language processor models. It is best to remove these from the raw text.
Unicode Normalization: Unicode normalization is necessary to remove unrecognized and often unnecessary characters from content that confuses the process. When posting a review, for example, people may use an emoji to communicate their thoughts. That is going to confuse the process, so it is necessary to get rid of that. That process is complex, but you can use an aspect-based sentiment analysis tool to help you through it.
Spelling: The next step in readying the process for aspect-based sentiment is to correct the spelling. Anyone who types can create a typo, and yet that simple word mistake can confuse your model. That is why you need to use a tool like pyspellchecker, a type of Python library, to check the spelling of the content.
Text Pre-Processing: Now that the text extraction and cleaning are complete, the next step in your NLP project is pre-processing. Every NLP project will include a modeling phase in which we train the model so that you can then use it for real-world data and receive accurate insights. A model will need numerical data, but when data is textual, we need to use text pre-processing instead. There are numerous components to this, including:
- Preliminaries such as sentence segmentation and word tokenization
- Stop word removal
- Stemming and lemmatization
- Removing digits and punctuation
- Lowercasing
- Normalization and language detection
- Code mixing
- Transliteration
- POS tagging
- Parsing data
- Coreference resolution
Latent Dirichlet Allocation (LDA) in Aspect Based Sentiment
If you have not yet done so, learn a bit about Latent Dirichlet Allocation (LDA), a type of topic modeling method that will uncover the central topics and their distributions across various documents.
By working through that process, you will be done choosing the aspects for each respective review. The next step is to create a pytorch-based model to predict aspect-based sentiment.
Aspect-Based Sentiment Analysis Data Set Model
With the aspect identification complete, it is necessary to create a model that will predict sentiments on the basis of those newly created aspects. You will need to work through:
- Configurations
- Dataset generator, including the creation of the vocabulary
- Word embeddings
- Initializing the model
- Training and K-fold cross-validation
- Inference is the next step in this process.
Using an Aspect-Based Sentiment Analysis Tool: Public Trained Okay?
One of the options you have for navigating this process a bit easier is to use public pre-trained models. Here is a good starting point: the DeBERTa fined-tuned over ABSA datasets. There are various tools out there that you could use, including necessary libraries.
To do so, you will need to install the proper libraries, such as the transformers library and the SentencePiece tokenizer. Many of the libraries’ models, like DeBERTa, will use this. In port, the necessary libraries are loaded, and two different models (for example, the absa_model and absa_tokenizer) are loaded. This allows you to test the pre-trained ABSA model.
The Importance of Using Aspect-Based Sentiment
As you work to understand the wide range of data types on the internet that could influence the decision-making of your product, you absolutely need to understand not just what a person is saying but what they are feeling, experiencing, and meaning in that statement. Aspect sentiment analysis can help you to achieve that.
When you utilize aspect-based sentiment analysis and pair that with high-powered web scraping with proxies, you gain the information you need without the frustration that often comes from the process. If you have not done so yet, take a moment to explore how Rayobyte can help you by providing you with access to proxies that can protect your identity and keep the data available to you. Contact us to learn more.
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