How To Build a Twitter Sentiment Analyser Using Natural Language Processing
Natural language processing is the capability of the computer program to comprehend the human language, both verbally and manually and then use it for communication. Computer systems use linguistics, computer science and artificial intelligence for this complex operation. After understanding the context of the textual or verbal content, they can use it to infer, analyse, and make something of their own. In simpler terms, they are trying to understand and use language just like a human.
Building a Twitter sentiment analyser
NLP is a part of a machine learning course with placement. You are trained to develop code for activities like these, where you will be building a Machine Learning model that will try to understand the sentiment behind a tweet. Using this Twitter sentiment analyser, you can try to understand which tweets have hate speech or objectionable speech in them. It could also be used to filter sexist and racist tweets as well. It is an activity that is related to supervised learning.
For this activity, you would need the following:
- Coding knowledge of Python.
- You will need to use various libraries of Python and natural language processing.
- A dataset consisting of tweets. This dataset can be downloaded from the Twitter API.
- Knowledge of three classifiers - logistic reasoning, Bernoulli Naïve Bayes and Support Vector Machine (SVM)
Coming to the dataset will contain various fields like:
- Twitter handles: The id of the user
- Ids: Unique tweet id
- Date: The tweet date
- Flag: It refers to the social platform's filtering response to indicate the query's polarity, i.e. is the tweet positive or negative? If no such response exists, then the default value of this response is NO QUERY.
- Text: The text of the tweet. This is the content that we have to process and comprehend the context.
- Stopwords: A list of stopwords or words that are irrelevant for processing is provided to the machine learning dataset so that these words are not used in the assignment.
The rest of the other fields will be removed or overlooked while the text will be processed for sentiment comprehension and reporting. This machine learning technique is used by all websites, mainly social media platforms, forums and dating apps, to filter and remove objectionable content. Along with the filtering script, the sentiment analyser is used to understand the milieu of the tweet.
What does the project pipeline contain?
The chronological steps that form the project pipeline for the machine learning assignment are given below:
- Import the required dependencies i.e. the ML libraries that are required to understand the emotion behind the tweet. For this, you could import the Seaborn library or the Wordcloud library.
- Read and load the dataset. The dataset will be loaded onto the ML model after cleaning the raw data and extracting the information relevant to the code development target.
- Exploratory data analysis. Analysing the data for the specific target variables. Which tweets have the data variables and which tweets do not have them? The empty values are treated as NO QUERY or null valued fields.
- Data visualisation of target variables. The visualisation of the usage of the target variables in a pictorial manner will tell how densely the emotional words are used. This will help in extracting the necessary language indicators that will help to understand the context of the tweet.
- Data pre-processing. After the visualisation has been done, the data will be further filtered for being split up and for training the machine learning model for future analysis of the tweets. Stemming and lemmatization are performed in this step which helps to reduce the language to its root form by understanding the meaning of the words.
- Splitting our data into train and test subsets. This is an intermediary step which will be necessary for the training of the model.
- Transforming dataset using TF-IDF vectorizer. This will help to evaluate the model with the help of the transformed data. The polarity of the words, either positive or negative will be processed for matching with the sample data. Here numerical values are given to various emotions.
- Function for model evaluation. The context will be understood in this stage based on the sample dataset and the inferred dataset. After that, a comparative analysis will be done which will help us to understand the extent of the polarity of the words.
- Model building. After the sample dataset has been analysed and processed for the context, this data will be used for the evaluation of future data.
- The assignment will be concluded with the necessary inferences from the experiment and analysis of the sample dataset.
Once you enrol for PG in data analytics, you will learn more about this in greater detail. Also, if you take admission with Imarticus Learning for a PG program in machine learning and artificial intelligence, you will participate in live projects that will help you understand how to manage professional responsibilities.
To sum up, if you plan to learn how to build Twitter sentiment analyser or similar programs, then learning natural language processing is the right first step. Here, you will learn the basics of AI and ML, which will help you build such an extensive program without any hassle.