Content Sentiment Analyzer

This tool analyzes the overall sentiment of your content before you post it! Check if you might come across with a negative tone, and switch up your wording based on your results.

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Inspiration

As social media users, we are often questioning how we come across on our various platforms. Am I professional enough on LinkedIn? Am I standing out enough on Twitter? I hope I don't sound like a negative Nancy..

In order to combat this, we wanted to develop a tool that would analyze word choice before posting on social media. This makes it easier for people with anxiety, newcomers to social media, or non-native English speakers to double check their tone and word choice before publicly sharing a thought. This application is particularly useful in situations where you want to discuss a controversial topic online while also coming across with a positive aura.

What it does

This tool enables users to pre-check their social media content to optimize their wording and phrasing, based on sentiment analysis and alternative word suggestions. In order to do this, it assigns a sentiment score to each word (positive, negative, or neutral) and displays an overall score for the post from a range to -100% (most negative) to 100% (most positive). It highlights the most positive and negative words in the message, with varying hues depending on the intensity of each word. This helps the influencer visualize how powerful their language is. The tool then suggests alternative words to make a more unique post.

How we built it

This tool is built on top of the following technologies and resources:

Challenges we ran into

We had roadblocks when resolving asynchronous promises and styling the user interface. These were overcome with extensive research cough StackOverflow cough and persistence.

Accomplishments that we're proud of

We were able to implement an algorithm that alters the hue that highlights positive and negative words, dependent on the intensity of the word. We used the intensity as a multiplier of the hue and manipulated the hexadecimal values of the colors to create a visually appealing user experience.

This was also our first time completing a React project start to finish, which provided a huge learning opportunity. Speaking of which...

What we learned

This was an opportunity for both of us to get more comfortable with React, particularly focusing on state management and modular development. It also exposed us to the process of researching various API and NPM library alternatives to solve a task at hand.

What's next for Content Sentiment Analyzer

We would like to hook into social media APIs and analyze previous posts to enable users to increase their sentiment scores over time. We also plan to contribute to the open source library that we leveraged, as we noticed room for improvement in its algorithms.

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