When you're building a content plan for digital marketing, you want to search high and low for the perfect topics and keywords that will resonate with your target audience. Fortunately, keyword extraction is a process that can help. In this article, we'll take a look at the operations behind this type of analysis, and how it's used in the context of content creation and, in particular, how it can be used with social media.

What is keyword extraction?

Keyword extraction is a form of data mining that involves identifying the most relevant words or phrases from user-generated content. In the context of social media, keyword extraction is performed on the content of platforms like Twitter, Facebook, Instagram, and others. Once these words are identified, content analysis is performed to simplify, summarize, and classify the content.

‍

Keyword extraction can involve supervised learning,  which requires labeled data to train models that can predict or classify keywords based on features extracted from the text. Conversely, it can involve unsupervised learning, in which no labeled data is required. Unsupervised learning methods rely on statistical and linguistic features to identify keywords based on their frequency and importance within the text.

Techniques and tools for keyword extraction

First comes a bit of data preprocessing. This is the preparation and cleaning of raw data to improve the quality and accuracy of the extraction process. We'll look at the processes first, and then the tools you can use to employ them.

Data preprocessing in keyword extraction from social media

Your first task is eliminating irrelevant characters, symbols, and formatting issues (e.g., HTML tags, special characters) from the text. All of the words should be in the correct case, too.

‍

Now, you're ready for tokenization, where the text is broken into individual words or tokens. This helps when it comes to analyzing the frequency and importance of each term.  

‍

Filter common words (e.g., "and," "the," "is") that don't carry significant meaning in the context of keyword extraction. These are known as "stop words" and can clutter the analysis.

‍

Reduce words to their base or root form (e.g., "running" to "run") through stemming or lemmatization. This helps in treating different forms of a word as the same term, improving the accuracy of keyword extraction. Synonyms or variations of terms should be merged to ensure that different representations of the same concept are considered as a single keyword.

‍

Exclude non-textual elements like images, videos, or other media types that do not contribute to the textual analysis. The data should be in a uniform format, which can be especially important for numerical data if it's involved in the text analysis.

‍

Finally, segment long texts into meaningful units or chunks, which can help in more accurate keyword extraction by focusing on specific sections or themes.

NLP tools for running text analytics on social media text

Now you're ready to do some analysis. Natural language processing tools are crucial for analyzing and extracting insights from the vast and diverse texts generated on social media platforms. These are the tools you can use to efficiently process unstructured data. They employ tasks such as semantic analysis, noise reduction, trend detection, and keyword extraction, which are crucial for understanding user behavior and adjusting engagement strategies.

Statistical methods

• Term frequency-inverse document frequency (TF-IDF). Measures the importance of a term in a document relative to its frequency in the entire corpus.
• Frequency analysis. Counts occurrences of terms or phrases to determine their relevance.

Linguistic approaches

• Part-of-speech tagging. Identifies nouns, verbs, adjectives, etc., to extract meaningful words.
• Named entity recognition (NER). Detects names of people, places, organizations, and other specific entities.

Machine learning models

• Topic modeling. Uses algorithms to identify themes within the text, then clusters the data by main topics and subtopics.
• Deep learning. Employs neural networks to capture complex patterns and contextual information.

‍

Some organizations combine statistical, linguistic, and machine learning approaches to improve accuracy. It all depends on your goals and bandwidth.

What are some challenges that come with keyword extraction from social media?

Keyword extraction from social media presents several distinct challenges that stem from the unique nature of social media content.

‍

One major issue is the informal and varied language used, including slang, abbreviations, and unconventional grammar. This linguistic diversity can make it difficult to accurately identify and extract meaningful keywords. Just think of how quickly nonsensical catch-phrases and ear-worms enter and exit our online lexicon.

‍

Social media texts are also pretty noisy, with a high volume of irrelevant, redundant, or off-topic content that can obscure valuable information. You'll often find bots or swarms of trolls that throw off the statistical significance of certain texts or phrases by spamming with (often inappropriate or nonsensical) posts.

‍

Another challenge is the context sensitivity of keywords. Words and phrases can have different meanings depending on their context, complicating the task of extraction and requiring sophisticated algorithms to sort out through sentiment analysis. Furthermore, the rapid and dynamic nature of social media means that trends and topics can shift quickly, requiring keyword extraction systems to be both timely and adaptable.

‍

The sheer volume of data generated on social media platforms also presents scalability issues, making it hard to process and analyze large datasets efficiently. Addressing these challenges requires a combination of advanced natural language processing techniques, machine learning models, and real-time processing capabilities.

Applications

Keyword extraction is an important step in lots of different processes, including:

Social media monitoring

Brands and organizations use keyword extraction to monitor mentions and conversations on social networks. By identifying trending topics and popular keywords, marketers can tailor their campaigns to align with current interests and preferences. This helps in creating more relevant and engaging advertisements.

Competitor analysis

By extracting keywords related to competitors, businesses can gain insights into their rivals’ strategies and performance, helping them to position themselves more effectively in the market. Rellify can provide expert competitive analyses. With a custom Relliverse™, we use deep machine learning to find topics and keywords that are already resonating with audiences in your specific industry or niche. That way, you can create the right content for the right people, bringing better results.  

Content curation

By relying on keyword extraction, and subsequent analysis of keywords related to user interests and interactions, you can build a content plan that's perfectly tailored to maximize your user engagement. Rellify can help with this, too. By providing data visualization, cluster analysis, and lots of other helpful AI-powered data analytics, you can find the right topics that will maximize your ROI.

Which NLP tools are best for social media text analysis?

Fortunately, there's no shortage of tools that can help with information extraction from social media. Check out the different features and interface with each solution, and find one that best matches your goals and preferences. Here are a few examples of popular tools.

‍

• NLTK (Natural Language Toolkit). NLTK provides a comprehensive suite of text processing libraries and resources, including tokenization, stemming, lemmatization, and text classification. Its highly customizable and includes lots of different linguistic resources and tools for different text analysis tasks, making it suitable for in-depth analysis.
• BERT (Bidirectional Encoder Representations from Transformers). BERT is a transformer-based model developed by Google that excels in understanding context and relationships in text. Its ability to capture nuanced meanings and context makes it highly effective for sentiment analysis, entity recognition, and understanding complex social media interactions.
• RapidMiner. RapidMiner is a data science platform that includes tools for text mining and sentiment analysis with a user-friendly interface. Its visual interface and integration capabilities make it accessible for non-programmers and useful for quickly building and deploying text analysis workflows.

How Rellify uses keyword extraction

Social media is just one way to find out what your target audience is talking about in their online communities. Search engines are another important focus, especially for B2B and B2C marketers.

‍

At Rellify, we focus on SEO, using deep machine learning models to process large sets of data that are relevant to your industry or niche. Keyword extraction is just one piece of the puzzle, and the rest is a breeze with a custom Relliverse. If you're ready to experience 10x the returns on your SEO efforts with 10x less effort, contact an expert at Rellify today for a free demo.