The Pros and Cons of using free datasets for Aspect-Based Sentiment Analysis
What is Aspect-Based Sentiment Analysis?
Aspect-based sentiment analysis (ABSA), also known as fine-grained sentiment analysis, is a technique used to analyze and understand the sentiment of specific aspects or entities in a text. This type of sentiment analysis goes beyond simply identifying overall sentiment, as it allows for a nuanced understanding of the feelings and opinions expressed about specific topics in a given text.
For example, consider the following review of a restaurant: “The food was amazing, but the service was terrible.” In this review, the overall sentiment is positive (the food was amazing), but the sentiment of the service is negative (the service was terrible). Traditional sentiment analysis techniques would only identify the overall sentiment of the review as positive, but ABSA allows us to understand that it’s mixed.
There are several steps involved in performing ABSA, including:
- Identifying the aspects or entities mentioned in the text. This step involves techniques such as named entity recognition and part-of-speech tagging to identify the specific nouns and noun phrases that represent said aspects or entities. For example, in the above restaurant review, the aspects might include “food” and “service”.
- Classifying text sentiment with respect to each identified aspect. Using sentiment analysis algorithms, text sentiment is classified for each identified aspect as positive, negative, or neutral. Using our restaurant review example: the sentiment with respect to “food” would be classified as positive, while the sentiment of “service” is classified as negative.
- Evaluating the results. Once the sentiments for each aspect have been classified, it’s important to evaluate the accuracy of the results by comparing the predicted sentiments with human-generated labels, or by using other evaluation metrics such as precision, recall, and F1 score.
ABSA has become vital in recent years as consumers and businesses alike have grown more interested in understanding the nuances of online reviews and social media posts. By analyzing the sentiment of specific aspects, businesses gain valuable insights into the strengths and weaknesses of their products and services and can improve their offerings for customers and stakeholders.
Advantages and disadvantages of Open-source datasets
Open-source datasets are important for sentiment analysis projects for several reasons. First and foremost, they provide researchers and developers with a common set of data for the development and evaluation of sentiment analysis models. This allows for accurate comparisons between different approaches and helps ensure that progress in the field is being made.
Secondly, open-source ABSA datasets are freely available, meaning anyone can access them without paying for a license or subscription. This can be especially beneficial for researchers and students who may not have the funds to purchase proprietary datasets.
Another advantage of open-source ABSA datasets is that they’re widely used and well-known in the natural language processing (NLP) community, meaning that there’s a wealth of information and support available for anyone using these datasets in their own projects. For example, many open-source ABSA datasets come with documentation on how to use the data effectively.
The most prominent source of freely available datasets for ABSA is SemEval (Semantic Evaluation): an ongoing series of evaluations of computational semantic analysis systems, organized under the umbrella of SIGLEX, the Special Interest Group on the Lexicon of the Association for Computational Linguistics. It covers a wide range of tasks, including sentiment analysis, semantic relatedness, and syntactic parsing, and is widely used in the NLP community as participation in SemEval evaluations is an important benchmark for many NLP systems. The SemEval Wikipedia entry and the ACL SemEval Wiki provide a more detailed historical overview.
Since its original release in 2014, it has been updated to include several domains such as laptop, restaurant, and hotel reviews spanning several languages such as English, Arabic, French, and German.
More info about the SemEval datasets can be found here.
As useful as they can be, a major disadvantage of open-source datasets is that they may not be as comprehensive as proprietary datasets. As anyone can contribute to them, the quality and quantity of the data may vary. This can make it difficult to use them for certain projects, especially those that require a large dataset or want to apply ABSA on a very specific domain.
Since open-source datasets are created and maintained by volunteers, another potential disadvantage is that they may not be updated as frequently as proprietary datasets, which are managed by professional teams. This makes it a challenge to use open-source datasets for tasks that require the most recent data.
How Defined.ai can help
While open-source ABSA datasets may be free and have a strong community supporting them, they can also be hit or miss for the reasons listed above, which is not a risk that businesses have the luxury to take. Defined.ai’s Data Marketplace offers top-notch commercial ABSA datasets that will give your projects a competitive edge. Our datasets are carefully curated by experts and constantly updated to the exacting standards our clients need. Plus, our platform hosts a variety of helpful tools and offers support to help clients make the most of our data.
Whether you’re working on a big NLP project or just need to get a modest sentiment analysis model jumpstarted, Defined.ai has your back. Check out our data samples for product reviews in English, Spanish, Chinese, and more here.