Abstract:
With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defence and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user’s tweets as having `positive’ or `negative’ sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic.
Existing System:
- In recent years, researchers preferably made the use of social data for the sentiment analysis of people’s opinions on a product, topic, or event. Sentiment analysis, also known as opinion mining, is an important natural language processing task. This process determines the sentiment orientation of a text as positive, negative, or neutral.
- Twitter sentiment analysis is currently a popular topic for research. Such analysis is useful because it gathers and classifies public opinion by analysing big social data. However, Twitter data have certain characteristics that cause difficulty in conducting sentiment analysis in contrast to analysing other types of data.
- Tweets are restricted to 140 characters, written in informal English, contain irregular expressions, and contain several abbreviations and slang words. To address these problems, researchers have conducted studies focusing on sentiment analysis of tweets .
- Twitter sentiment analysis approaches can be generally categorized into two main approaches, the machine learning approach, and a lexicon-based approach. In this study, we use machine learning techniques to tackle twitter sentiment analysis.
Proposed System:
we present the methodology used in this study. The proposed system is basically composed of four main modules. Thirst module is data acquisition, which is a process of gathering labelled tweets to perform sentiment analysis; the second module, this dataset undergoes various steps of pre-processing to transform and renew tweets into a data set that can be easily used for subsequent analysis. The third module concerns the extraction of relevant features for building a classification model. Then, the balancing and scoring tweets technique is illustrated. The last module is applying different machine learning classifiers that classify the tweets into high positive, moderate positive, neutral, moderate negative, and high negative. Figure 1 shows the various steps performed for sentiment analysis using machine learning algorithms.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
