WordCloud and Sentiment Analysis with Python

One of the most popular concepts of our day is the word cloud and the work done on it. People use the nltk library to experiment with the word cloud. The aim here is to process the processes before the natural language processing phases. Since the Python programming language reaches a wider audience every day, the variety of projects made with nltk is increasing. Beginners often analyze tweets posted on Twitter on any topic and make visualizations, analyzes and inferences from it. While creating a word cloud, one of the key points is the number of repetitions. The more the word repeats, the more prominent it becomes in the word cloud. I tried to explain your research with the image below. Here the part indicated with q is the part of the word or phrase you are looking for. To do this, you must first have a Twitter Developers account. In addition, you will be providing the link here as we will pull it from here. Through the link, you can pull tweets to your local workplace and take action on them.

As can be seen in the photo above, the most critical part of the job is to set up the def structure well and decide on which variables to label. You can save the token and key values ​​somewhere and withdraw from there. Here, I did not show the key values ​​due to privacy, but once you have a Twitter Developer account, you can access these values ​​yourself and make the necessary assignments. If you want to access specific values ​​while analyzing on Twitter, you can make custom searches. I will put the necessary documentation in the Resources section, you can review it according to your request and integrate it into your own code and analysis. If I need to give extra information, there are several methods in nltk we will use. One of the ones I use individually is “stopwords” and the other is “wordnet”. You can change the English option according to the language you want to work on. It is a comprehensive English language in terms of word strength and effectiveness. If you are working on your own language and have a great collection of words, you can specify it in the comments tab. Thus, we can keep the interaction high and increase the yield rate. You can observe the part I explained here in the image below.

I chose the word ‘samsung’ for the Word Cloud study. By entering the abbreviation of the language option in the Lang section, you can pull the hashtag data you have chosen to your work area. At first we add the necessary libraries, and then we can change the background color we will work with according to your personal wishes. In addition, if you set the plt.axis value to “on”, you can observe the frequency of repeating words. Since I did this with the highlighting method myself, I found it unnecessary to show the axes as extra. What I do here is to set up a basic wordcloud structure and to gain something even at your entry level. If you have a career planning for natural language processing, you can see it as a start and continue to improve by following the necessary notes. The word cloud structure is the bottom tab of these jobs. While I teach you as a junior, I also continue to work in this field myself. The natural language processing career is an area where you need to stay up-to-date on what others are doing, how they code, and what projects are done on platforms such as large-scale resource browsing and github.

I’ll show you an entry-level sentiment analysis, which I will mention in the last part. Before starting what I will explain here, when writing code in Python, you need to set up the def patterns well and feed your code into it here. Accelerating functional processes and writing an understandable, scalable code for people who will come after you while working in the future will benefit everyone. By writing clean code with def structures, you can easily transfer what you do there to the person who comes for you. Going back to the sentiment analysis, here we can already do this scoring work via the textblob library. TextBlob classifies it as a result of the content of tweets sent on Twitter and the positive – negative words of this content. After this classification, it gives you a ready-made column for analysis. You can analyze this according to your wishes and try different studies. For example, you can chart it, observe the number of repetitive words and take the differences with these values ​​and integrate the background into a picture you have edited yourself.