Sentiment Analysis using Neo4J

Arshit Arora
3 min readApr 25, 2023


Are you a student or aspiring data scientist looking for a project to showcase your skills? Look no further than sentiment analysis! In this post, we’ll show you how to use ArgonDB to build a sentiment analysis model that can classify the sentiment of text data.

Sentiment analysis is the process of using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from text data. It involves classifying the sentiment of the text as positive, negative, or neutral. Sentiment analysis has many applications, from social media monitoring to customer feedback analysis.

Now, let’s talk about ArgonDB. ArgonDB is a lightweight, fast, and scalable NoSQL database that is designed for real-time applications. It provides a flexible data model that can handle complex data structures, making it ideal for storing and querying unstructured data such as text.

To build a sentiment analysis model using ArgonDB, you will need to follow these steps:

1) Collect and preprocess your data: The first step is to collect data that you will use to train and test your model. This data can be in the form of customer reviews, tweets, or any other text data. Once you have your data, you will need to preprocess it by removing stop words, stemming, and tokenizing.Data preprocessing is an essential step in building a sentiment analysis model. Preprocessing involves cleaning the data and transforming it into a format that can be used by machine learning algorithms. In the case of sentiment analysis, preprocessing includes removing stop words, which are common words that do not add much value to the meaning of the text, such as “and,” “the,” and “of.” Stemming is another preprocessing step that involves reducing words to their root form, such as “run” and “running” to “run.” Finally, tokenizing involves breaking the text data into smaller units, such as words or phrases, which can be used as features for machine learning models.

2) Build your model: After preprocessing your data, you will need to build your model. There are many different machine learning algorithms that you can use for sentiment analysis, including logistic regression, naive Bayes, and support vector machines. You will need to train your model using your preprocessed data and evaluate its performance using metrics such as accuracy, precision, and recall.Machine learning algorithms are used to build a model that can learn from the preprocessed data and predict the sentiment of new text data. There are many different machine learning algorithms that can be used for sentiment analysis, each with its strengths and weaknesses. For example, naive Bayes is a simple algorithm that works well with small datasets, while support vector machines are more complex and work well with larger datasets.

3) Store your data in ArgonDB: Once you have your model, you will need to store your data in ArgonDB. You can use ArgonDB’s flexible data model to store your preprocessed data and your trained model.ArgonDB is a NoSQL database that is designed to store and query unstructured data such as text. Its flexible data model allows you to store complex data structures and query the data using a powerful query language. Storing your preprocessed data and trained model in ArgonDB allows you to easily access and query the data when classifying the sentiment of new text data.

4) Query your data: Finally, you will need to query your data to classify the sentiment of new text data. You can use ArgonDB’s powerful query language to search and retrieve data from your database.Once you have stored your preprocessed data and trained model in ArgonDB, you can use the query language to classify the sentiment