Talkwalker's complete social media analysis tool (2024)

FAQs

How accurate is Talkwalker sentiment analysis? ›

With an average 90% accuracy, this measures your brand sentiment, and detects the themes that are positively or negatively impacting your business.

It allows you to track, analyze, and respond to social media data related to your brand, industry, or specific topics.

Great tool for the combination of traditional and social monitoring. I've been using Talkwalker for four years now. Great tool for both traditional and social media monitoring. I personally think it's the best tool that offer both.

Statistical machine learning models like Naive Bayes Classifier, Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gradient Boosting Machines (GBM) are all valuable for sentiment analysis, each with their strengths.

Talkwalker was also the first to add social listening video recognition, to detect brand logos within social media videos. Use our consumer intelligence platform with fully-integrated visual listening to make sure you get the full picture on social and online.

Identify your brand mentions in videos and images. Even without accompanying text. See exactly how consumers engage with your brand and products. Protect your business with robust crisis monitoring and competitor intelligence.

One example of social media analysis involves looking at your audience engagement through reactions on Facebook posts and measuring conversions by tracking the number of clicks on LinkedIn posts.

What is the most used tool of social media? ›

Google Analytics gives you the tools, free of charge, to understand the customer journey and improve marketing ROI.

Setting a baseline sentiment accuracy rate

When evaluating the sentiment (positive, negative, neutral) of a given text document, research shows that human analysts tend to agree around 80-85% of the time. This is the baseline we (usually) try to meet or beat when we're training a sentiment scoring system.

The dataset used in the experiment consisted of a collection of financial news articles. The CNN model was trained on this dataset and achieved an accuracy of 80.20%, precision of 78.35%, recall of 74.80%, and an F1-score of 76.05%.

As you can see, the overall accuracy of this rule-based sentiment analysis model is 79%.

One limitation is that they often ignore contextual information of sentences, which can affect the accuracy of sentiment analysis . Another limitation is that lexicon-based approaches do not consider the sentiment information embedded in sentiment words, which can lead to incomplete analysis .