Top 8 Sentiment Analysis Datasets in 2024

Sentiment analysis is a great way to understand the customers’ feelings toward a company and to see if they are associated with sales, investments, or agreements. Ensuring a reliable sentiment analysis depends on many factors, and one of its building blocks is the dataset used to train the models. However, finding the right dataset is easier said than done.

This article highlights the top sentiment analysis datasets to train your algorithms for more efficient and accurate sentiment analysis.

Although the quantity of the data is crucial, the quality or relevancy is also essential to have reliable results. For instance, if a retail company uses a dataset with financial jargon to train a customer sentiment analysis model, the algorithm may not provide reliable results as the words which the algorithm evaluates will be from a financial context.

So, having the right training dataset is crucial in evaluating the reviews, as you can develop new strategies with the insights you gather. Here we list the top eight sentiment analysis datasets to help you train your algorithm to obtain better results. 

1. Amazon Review Data

This dataset contains information regarding product information (e.g., color, category, size, and images) and more than 230 million customer reviews from 1996 to 2018. The reviews are labeled based on their positive, negative, and neutral emotional tone.

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They can help your company with sentiment analysis services using a pre-determined training dataset to understand your customers better.

2. Stanford Sentiment Treebank

Most sentiment analysis tools categorize the sentences by giving sentiment scores to each word without considering the sentence as a whole. Here, you can find almost 10,000 reviews on movies with sentiment scores ranging from 1 to 25. While 1 represents the most negative reviews and 25 corresponds to the most positive ones. 

Figure 1. An example of a movie review and the sentiment score of each aggregate

3. Financial Phrasebank

The financial phrase bank dataset contains almost 5000 English sentences from financial news, and all sentences are classified based on their emotional tones as either positive, negative, or neutral. All the data is annotated by researchers knowledgeable in the finance domain. 

Figure 2. Examples of the sentences from financial news and the corresponding sentiment label class

4. Webis-CLS-10 Dataset

Webis cross-lingual sentiment dataset includes 800.000 Amazon product reviews in English, German, French, and Japanese. Its multilingual nature allows for reaching more audiences and conducting comprehensive analyses. 

5. CMU Multimodal Opinion Sentiment and Emotion Intensity 

Not only do texts contain customers’ sentiments regarding services or products, but they can also be detected from videos or audio. CMU dataset includes multimodal data extracted from Youtube videos, such as the sentences and the voice tone used. 

Figure 3. The word cloud of the topics mentioned in the videos

6. Yelp Polarity Reviews

This open-source dataset includes more than 500,000 training samples consisting of consumer reviews, ratings, and recommendations. The polarity score of each sentence is determined, and the keywords requested can be extracted. 

7. WordStat Sentiment Dictionary

Wordstat Sentiment Dictionary classifies sentiments as negative or positive and combines three dictionaries: Harvard IV Dictionary, Regressive Imagery Dictionary, and Linguistic and Word Count Dictionary. The combination of different dictionaries allows for identifying synonyms and word patterns automatically.

8. Sentiment Lexicons For 81 Languages

Although English is the most spoken language globally, it is also crucial to analyze the sentiment of other language speakers. This dataset includes 81 languages such as Chinese, Spanish, and German, so it offers a variety of data from different languages and represents the worldwide sentiment better.

Using a trained dataset to run your algorithm is essential in sentiment analysis. So, working with reliable sources matters. 

You can also check our data-driven list of sentiment analysis services. 

Further Reading

• Consumer Insights: Why Is It Essential & Top 4 Data Sources
• How to Benefit From Social Media Sentiment Analysis?
• Top 5 Sentiment Analysis Challenges & Solutions

If you have further questions about sentiment analysis, do not hesitate to reach us:

This article was originally written by former AIMultiple industry analyst Begüm Yılmaz and reviewed by Cem Dilmegani.