Plotting Frequency of Words

Plotting Frequency of Words Now Let's Look at What We Can Do Further: Analyzing WordFrequencies We shall discuss the idea of word frequencies and how to analyze them in this article. In several disciplines, including linguistics, literature, and data science, word frequencyanalysis is a helpful tool. We may learn about a corpus of text's features, such as itsvocabulary size, the frequency with which certain words are used, and the text's overalltheme, by looking at word frequencies. Counting Word Frequencies Counting the instances of each word in a corpus is the initial stage in the analysis of word frequencies. The Python counter object from the collection package can be used toaccomplish this. A list of words is input into the counter object, which then returns an objectwith the frequency of each word. For instance, we can use the counter object to determinehow frequently the word "movie" appears in a database of movie reviews that contains 1.6million words. In the above example, "movie" appears 5,771 times in the corpus. This information can be useful in determining the most frequently used words in the corpus, which can give usan idea of the overall theme of the text. Plotting Word Frequency With the help of matplotlib, we can visualize the distribution of words once we know the frequency of each word in a counter object. To examine the distribution's shape, we sortedthe word counts in our notebook and plotted their values on logarithmic axes. If we arecomparing two or more data sets, this visualization is especially helpful. A flatter distribution indicates a large vocabulary, while a peak distribution indicates a restricted vocabulary, often due to a focused topic or a specialized language. Therefore, byplotting the distribution of word frequencies, we can gain insights into the characteristics ofa corpus and the usage of specific words. Histograms of Word Frequencies

Another way to visualize word frequencies is to create histograms. Histograms can be used to display the distribution of a dataset and are particularly useful for visualizingfrequency distributions. In our notebook, we used the filtered words list to create a counter object, which we then used to create histograms of the word frequencies. By creating histograms, we canvisualize the distribution of word frequencies and gain insights into the characteristics of acorpus. Filtering Useless Words One important step in analyzing word frequencies is to filter out useless words. Useless words are words that do not add any meaning to a sentence, such as "the," "and," and "a."These words are also known as stop words. In our notebook, we used a for loop to filter out useless words from the movie review database. By filtering out useless words, we were able to reduce the amount of words inthe corpus to about 710 thousand. This step is crucial in obtaining a more accurate analysisof the corpus and the usage of specific words. Conclusion In summary, word frequency analysis is a useful tool that can be used to learn more about the properties of a corpus of text and is effective in many different domains. We canidentify the most commonly used terms and get a sense of the text's general subject bycounting the frequency of each word in a corpus. We may see the properties of a corpusand the usage of particular terms by plotting the distribution of word frequencies onlogarithmic axes and making histograms. In order to get a more accurate analysis of thecorpus, it is crucial to remove pointless words. If you want to learn more about word frequency analysis and how to use Python for text analysis, there are many resources available online. Python has several libraries, such asNLTK and spaCy, that can be used for text analysis. With these tools, you can analyze textdata and gain insights that can be useful in many fields.