Sentiment Analysis Social Media Tools

Sentiment Analysis is a powerful tool for analyzing and improving your brand’s online presence. But it can be difficult to know where to start, especially if you’re new to the world of social media.

Luckily, we’ve compiled a list of some of the best social media tools that offer sentiment analysis capabilities so that you can begin tracking, analyzing, and improving your brand’s online presence right away!

Sentiment Analysis Social Media Tools

1. HubSpot’s Service Hub

HubSpot’s Service Hub tools include a customer feedback tool that can break down qualitative survey responses and evaluate them for positive or negative intent. It uses NPS® surveys to clarify whether a customer’s review was good or bad and organizes them based on their sentiment. Users analyze the results by looking at one comprehensive dashboard that includes charts and graphs which provide an overview of customer satisfaction.

HubSpot’s Service Hub suite can also analyze customers on an individual basis. You can integrate your CRM with Service Hub and review survey responses from specific contacts in your database. That way, your team can quickly identify customers who are unhappy and follow up with them after they’ve had a negative experience with your brand.  Remember, 58% of customers will stop doing business with you if your company falls short of their expectations.  this gives your team an opportunity to intercept unhappy customers and prevent potential churn.

Price: $45/month for Starter Plan, $360/month for Professional Plan, $1,200/month for Enterprise Plan

2. Talkwalker

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Talkwalker’s “Quick Search” is a sentiment analysis tool that’s part of a larger customer service platform. This tool works best with your social media channels because it can tell you exactly how people feel about your company’s social media accounts. Quick Search looks at your mentions, comments, engagements, and other data to provide your team with an extensive breakdown of how customers are responding to your social media activity. This helps your team plan and produce effective campaigns that captivate your target audience.

Price: $9,000/year for Listening Plan, Analytics Plan pricing available upon request, Research Plan pricing available upon request 

3. Repustate

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Repustate has a sophisticated text-analysis API that accurately assesses the sentiment behind customer responses. Its software can pick up on short-form text and slang like lol, rofl, and smh. It also analyzes emojis and determines their intention within the context of a message. For example, if I use a 😉 emoji, Repustate tells you if that’s a positive or negative symbol based on what it finds in the rest of the conversation.

Repustate also lets you customize your API’s rules to have it filter for language that may be specific to your industry. If there’s slang or alternate meanings for words, you can program those subtleties into Repustate’s system. This way you have full control over how the software analyzes your customers’ feedback.

Price: $299/month for Standard Plan

4. Lexalytics

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Lexalytics offers a text-analysis tool that’s focused on explaining why a customer is responding to your business in a certain way. It uses natural language processing to parse the text, then runs a sentiment analysis to determine the intent behind the customer’s message. Finally, Lexalytics concludes the process by compiling the information it derives into an easy-to-read and shareable display. While most sentiment analysis tools tell you how customers feel, Lexalytics differentiates itself by telling you why customers feel the way that they do.

Price: Pricing available on request

5. Critical Mention

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Critical Mention is different than the other options on this list because it analyzes news and other publications that reference your business. This way, you can see the sentiment behind stories that are rapidly surfacing to the public. Since news coverage is now a 24/7 affair, it helps to have software that can monitor the internet and alert you to any buzz your business is making.

Critical Mention can even alert you to stories that appear on television. You can search through video files for mentions of your company and easily clip videos to share with other employees. If your business gets positively mentioned on a live broadcast, quickly access the video segment and share it on your social media channels. This can help you create effective online content that capitalizes on timely marketing opportunities.

Price: Pricing available on request

6. Brandwatch

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One of the coolest features that Brandwatch provides is its “image insights” tool which can identify images associated with your brand. For example, say you upload an image of your brand’s logo. Brandwatch surfs the web for images that include that logo. Then, it compiles the images into a list and highlights exactly where your brand’s logo is appearing.

Additionally, Brandwatch’s software provides interesting insights into each image it finds. This includes metrics like mention volume, aggregate followers, and latest activity. With Brandwatch, your team sees where your brand’s images are appearing and how those images are performing with your target audience.

Price: Pricing available upon request

7. SociaKl Mention

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Social Mention is a free social media analysis tool that provides users with one of the best bangs for their buck. First, users don’t have to create an account or download software. Instead, you just need to navigate to their site and search for your keyword like you would with any search engine. Upon entering your search, Social Mention pulls data about your keyword from every social media site and compiles it into a comprehensive summary.

This summary isn’t primitive, either. It can tell you useful things like the ratio of people speaking positively about your keyword versus those who are speaking of it negatively. It can also tell you what percentage of people are likely to continue mentioning your keyword and how popular your brand is on social media. While you can’t really analyze individual pieces of data, Social Mention is a great option for people looking to get a brief synopsis of their social media reputation.

Price: Free

8. Sentiment Analyzer

Working with Sentiment Analyzer is a breeze. Simply navigate to their site, copy the text you want to analyze, and paste the text into the box. Select “Analyze!” and the website will evaluate your text and give you a “sentiment score.”

While that might sound like magic, Sentiment Analyzer uses “computational linguistics and text mining” to determine the sentiment behind your piece of text. It then compounds and compares its findings to produce an overall score. This makes it a great tool for companies looking to quickly decipher the intent behind a confusing response from a customer.

Price: Free

9. MAXG

MAXG is a HubSpot integration that analyzes customer data found in your CRM. It uses industry benchmarks to compare your company’s information against others in your marketplace, so you know how your business is performing compared to your competitors. 

MAXG can analyze a wide range of quantitative and qualitative data, including CTA’s, blog posts, and emails. And, with its insightful recommendations tool, it won’t just provide you with a clear look at how customers are interpreting your content, but it’ll also make suggestions for how you can improve engagement.  

Price: Free for Basic Plan, $29/month for Pro Plan, $99/month for Agency Plan

10. Social Searcher

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Social Searcher is a social media monitoring platform that includes a free sentiment analysis tool. All you have to do is search for a keyword, hashtag, or username, and Social Searcher will tell you whether or not the buzz surrounding this topic is positive or negative. It also breaks reports down by social media platforms, so you can see exactly how your brand is performing across different apps and channels.  

Price: Free to Start, 3.49 €/month for Basic Plan, 8.49 €/month for Standard Plan, 19.49 €/month for Professional Plan

11. Rosette

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Rosette is great for international businesses because it can review text-based data in over 30 different languages. This means you won’t have to translate conversations before you upload them, which is not only faster but ensures greater accuracy. Since most customers will use shorthand or slang, third-party translation tools can inadvertently change the meaning of their text. With Rosette, it’s system is built to analyze text in the language that it’s written, so you won’t lose any valuable feedback even if it’s written informally.

Price: $100/month for Starter Plan, $400/month for Medium Plan, $1,000/month for Large Plan 

12. MonkeyLearn

MonkeyLearn is a sentiment analysis tool that’s easy to customize. All you have to do is create categorization tags then manually highlight different parts of the text to show what content belongs to each tag. Over time, the software learns on its own and can process multiple files simultaneously. 

MonkeyLearn also provides its customers with a free “Word Cloud” tool that tells them what words are used most frequently within each categorization tag. This can help businesses discover common customer roadblocks by looking for repeat mentions of specific products or services. If you notice one product is consistently listed under a negative categorization tag, this would suggest there’s an issue with that product that customers are unhappy about. 

Price: Starts Free, $299/month for Team Plan, $999/month for Business Plan

Tired of interpreting your customers’ responses in the first place? Learn how to get better feedback by reading these tips for improving your customer feedback survey.

sentiment analysis tools python

Sentiment analysis is one of the most common NLP tasks, since the business benefits can be truly astounding. And Python is often used in NLP tasks like sentiment analysis because there are a large collection of NLP tools and libraries to choose from.

How to Do Sentiment Analysis in Python
If you have a good amount of data science and coding experience, then you may want to build your own sentiment analysis tool in python. .Many open-source sentiment analysis Python libraries , such as scikit-learn, spaCy,or NLTK. VADER (Valence Aware Dictionary for Sentiment Reasoning) in NLTK and pandas in scikit-learn are built particularly for sentiment analysis and can be a great help. Or take a look at Kaggle sentiment analysis code or GitHub curated sentiment analysis tools.

Another option that’s faster, cheaper, and just as accurate – SaaS sentiment analysis tools. Remove the hassle of building your own sentiment analysis tool from scratch, which takes a lot of time and huge upfront investments, and use a sentiment analysis Python API.

In this sentiment analysis Python example, you’ll learn how to use MonkeyLearn API in Python to analyze the sentiment of Twitter data.

MonkeyLearn provides a pre-made sentiment analysis model, which you can connect right away using MonkeyLearn’s API. Read on to learn how, then build your own sentiment analysis model using the API or MonkeyLearn’s intuitive interface.

Sentiment Analysis Python Tutorial
First of all, sign up for free to get your API key. Then, install the Python SDK:

pip install monkeylearn
You can also clone the repository and run the setup.py script:

$ git clone git@github.com:monkeylearn/monkeylearn-python.git
$ cd monkylearn-python
$ python setup.py install
And that’s it for setup.

You’re ready to run a sentiment analysis on Twitter data with the following code:

from monkeylearn import MonkeyLearn

ml = MonkeyLearn(‘<>’)
data = [‘I love everything about @Zendesk!’, ‘There’s a bug in the new integration]
model_id = ‘cl_pi3C7JiL’
result = ml.classifiers.classify(model_id, data)

print(result.body)
The output will be a Python dict generated from the JSON sent by MonkeyLearn, and should look something like this example:

[{
‘text’: ‘I love everything about @Zendesk!’,
‘classifications’: [{
‘tag_name’: ‘Positive’,
‘confidence’: 0.993,
‘tag_id’: 33767179
}],
‘error’: False,
‘external_id’: None
}, {
‘text’: ‘There’s a bug in the new integration’,
‘classifications’: [{
‘tag_name’: ‘Negative’,
‘confidence’: 0.979,
‘tag_id’: 33767178
}],
‘error’: False,
‘external_id’: None
}]
We return the input text list in the same order, with each text and the output of the model. Now, you’re ready to start automating processes and gaining insights from tweets.

Here’s full documentation of MonkeyLearn API and its features.

Now that you know how to use MonkeyLearn API, let’s look at how to build your own sentiment classifier via MonkeyLearn’s super simple point and click interface.

How to Build A Sentiment Analysis Classifier to Call with Python
It’s important to remember that machine learning models perform well on texts that are similar to the texts used to train them. For example, if you train a sentiment analysis model using survey responses, it will likely deliver highly accurate results for new survey responses, but less accurate results for tweets.

Generic sentiment analysis models are great for getting started right away, but you’ll probably need a custom model, trained with your own data and labeling criteria, for more accurate results.

With MonkeyLearn, building your own sentiment analysis model is easy. Just follow the steps below, and connect your customized model using the Python API.

Side note: if you want to build, train, and connect your sentiment analysis model using only the Python API, then check out MonkeyLearn’s API documentation.

1. Create a text classifier
   Go to the dashboard, then click Create a Model, and choose Classifier:

The option to choose Classifier or Extractor for your model (that you’ll call with Python).
Choose sentiment analysis as your classification type:

The option to choose Topic Classification, Sentiment Analysis, or Intent Classification. Click Sentiment Analysis to build your sentiment analyzer for this Python sentiment analysis tutorial.

2. Upload your training dataset
   The single most important thing for a machine learning model is the training data. Without good data, the model will never be accurate. As the saying goes, garbage in, garbage out. Upload your Twitter training data in an Excel or CSV file and choose the column with the text of the tweet to start importing your data.

We used MonkeyLearn’s Twitter integration to import data. However, if you already have your training data saved in an Excel or CSV file, you can upload this data to your classifier.

The option to upload sentiment analysis training data from a number of sources.
If using the Twitter integration, search for a keyword or brand name. In this example we searched for the brand Zendesk. Next, choose the column with the text of the tweet and start importing your data.

Tweets related to Zendesk.

3. Train your sentiment analysis model
   In this step, you’ll need to manually tag each of the tweets as Positive, Negative, or Neutral, based on the polarity of the opinion. After tagging the first tweets, the model will start making its own predictions, which you can approve or overwrite.

Tag data.

4. Test your Twitter sentiment classifier
   Once you have trained your model with a few examples, test your sentiment analysis model by typing in new, unseen text:

Test model.
If you are not completely happy with the accuracy of your model, keep tagging your data to provide the model with enough examples for each sentiment category. In this case, for example, the model requires more training data for the category Negative:

Showing statistics of the sentiment analysis model.
Remember, the more training data you tag, the more accurate your classifier becomes. You can keep training and testing your model by going to the ‘train’ tab and tagging your test set – this is also known as active learning and will improve your model.

5. Call your Sentiment Analysis Model with Python
   Once you’re happy with the accuracy of your model, you can call your model with MonkeyLearn API.

Perform sentiment analysis on your Twitter data in pretty much the same way you did earlier using the pre-made sentiment analysis model:

from monkeylearn import MonkeyLearn

ml = MonkeyLearn(‘<>’)
data = [‘I love everything about @Zendesk!’, ‘There’s a bug in the new integration’]
model_id = ‘<>’
result = ml.classifiers.classify(model_id, data)

print(result.body)
And the output for this code will be similar as well:

[{
‘text’: I love everything about @Zendesk!,
‘classifications’: [{
‘tag_name’: ‘positive’,
‘confidence’: 0.836,
‘tag_id’: 103237939
}],
‘error’: False,
‘external_id’: None
}, {
‘text’: ‘There’s a bug in the new integration’: [{
‘tag_name’: ‘negative’,
‘confidence’: 0.924,
‘tag_id’: 103237938
}],
‘error’: False,
‘external_id’: None
}]
Do Sentiment Analysis the Easy Way in Python
Sentiment analysis is a powerful tool that offers huge benefits to any business. And now, with easy-to-use SaaS tools, like MonkeyLearn, you don’t have to go through the pain of building your own sentiment analyzer from scratch. And with just a few lines of code, you’ll have your Python sentiment analysis model up and running in no time.

With MonkeyLearn, you can start doing sentiment analysis in Python right now, either with a pre-trained model or by training your own. Get started with

Get started with MonkeyLearn’s API or request a demo and we’ll walk you through everything MonkeyLearn can do.

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