How can machine learning be used to analyze customer sentiment on social media?

This article was an early beta test. See all-new collaborative articles about Machine Learning to get expert insights and join the conversation.

Sentiment analysis, also known as opinion mining, is the process of identifying and extracting subjective information such as emotions and opinions. Sentiment analysis can help businesses and organizations understand how their customers and competitors perceive their products or services, and how they react to different situations and scenarios. Machine learning can help automate the process of sentiment analysis by discovering patterns and relationships amidst data from social media platforms. Oftentimes, machine learning will allow businesses to adapt quickly to changes in customer sentiment and provide personalized recommendations to users. Here are some of the machine learning methods used for social media sentiment analysis today.

1. Supervised learning: Supervised methods rely on labeled data, such as tweets or posts annotated with positive, negative or neutral sentiments, to train and test sentiment classifiers, such as logistic regression, decision trees or neural networks. Through such training, machine learning models will be able to more accurately detect which posts were created with positive or negative connotations. Supervised methods can achieve high accuracy and performance, but they require a large amount of labeled data, which can be costly, time-consuming, and labor-intensive to obtain and maintain. They also tend to be domain-specific and less generalizable to other social media platforms.

2. Unsupervised learning: Unsupervised methods of learning for machine learning algorithms do not require labeled data, but instead use intrinsic features of the data, such as word frequencies or polarities. This will cluster and score the data based on their sentiments. While unsupervised methods can handle large and diverse data sets, they may also miss subtle and nuanced sentiments. They also depend on the quality and availability of external resources, such as sentiment dictionaries or ontologies, which may not capture the full range and variation of social media language.

3. Semi-supervised learning: Semi-supervised methods combine both labeled and unlabeled data, and use techniques such as bootstrapping or active learning, to leverage the strengths and overcome the limitations of both supervised and unsupervised methods. Semi-supervised methods can reduce the cost of data annotation, and improve the robustness and generalizability of sentiment analysis. But they may also introduce noise and inconsistency, and can require careful selection and optimization of parameters.

4. Data granularity and modality: Granularity refers to the unit or scope of the data being analyzed, such as a document or sentence, while modality is the mode or channel of the data, such as text, image, video or audio. Different levels of granularity and types of modality within the dataset may also require different machine learning models, and capture different dimensions of the users’ sentiments. For example, document-level sentiment analysis may use word embedding models to represent the overall sentiment of a tweet or a post. Meanwhile, image- or video-based sentiment analysis may use computer vision or multimodal fusion techniques to analyze the visual and acoustic aspects of the data.

Explore more

• 4 Applications of Machine Learning in Sentiment Analysis by Begüm Yılmaz for AIMultiple
• Conducting Social Media Sentiment Analysis: A Working Example by Vinay Dabhade for Express Analytics
• What is Sentiment Analysis? Using NLP and ML to Extract Meaning by Maria Korolov for CIO

This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.