3. U N DE R T H E G U I DANCE O F
DR.M.J.ABINASH MCA. ,M.TECH., PH.D.,
A S S I S T A N T P R O F E S S O R , H O D
D E P A R T M E N T O F I N F O R M A T I O N T E C H N O L O G Y
A K C A S
4. FRONT END
Tkinter – python web application framework
BACKEND
Python
(Python can be used for both frontend and backend development, depending
on the context and the frameworks or libraries used.)
5. * E m p l o y s n a t u r a l l a n g u a g e p r o c e s s i n g ( N L P )
t e c h n i q u e s f o r t h o r o u g h t e x t a n a l y s i s .
* U t i l i z e s s e n t i m e n t a n a l y s i s a l g o r i t h m s t o e v a l u a t e
t h e s e n t i m e n t p o l a r i t y o f r e s t a u r a n t r e v i e w s .
* P r e s e n t s s e n t i m e n t a n a l y s i s r e s u l t s v i s u a l l y f o r
e a s e o f i n t e r p r e t a t i o n .
* E n a b l e s s t a k e h o l d e r s t o u n d e r s t a n d c u s t o m e r
o p i n i o n s o n v a r i o u s a s p e c t s o f t h e r e s t a u r a n t ,
i n c l u d i n g f o o d q u a l i t y, s e r v i c e , a m b i a n c e , e t c .
* F a c i l i t a t e s d a t a - d r i v e n d e c i s i o n - m a k i n g f o r
r e s t a u r a n t m a n a g e m e n t t o e n h a n c e c u s t o m e r
s a t i s f a c t i o n a n d o v e r a l l b u s i n e s s p e r f o r m a n c e .
ABSTRACT
6. USING ALGORITHM
Tokenization:
Description: Tokenization breaks text into smaller units, such as
words, phrases, or symbols.
Purpose: Facilitates text analysis by converting raw text into
manageable units for further processing.
Algorithm: Simple tokenization splits text based on whitespace or
punctuation marks.
7. Part-of-Speech (POS) Tagging:
Description: POS tagging assigns grammatical tags (e.g., noun, verb, adjective)
to words in a sentence.
Purpose: Helps analyze sentence structure and grammatical relationships
between words.
Algorithm: Rule-based approaches or supervised learning algorithms, such as
Hidden Markov Models (HMMs) or Recurrent Neural Networks (RNNs), are
commonly used.
8. Sentiment Analysis:
Description: Sentiment analysis determines the sentiment
expressed in a piece of text, such as positive, negative, or neutral.
Purpose: Extracts opinions, emotions, or attitudes from text
data for various applications, including customer feedback
analysis and social media monitoring.
Algorithm: Can be performed using rule-based approaches,
machine learning classifiers (e.g., Naive Bayes, Support Vector
Machines), or deep learning models (e.g., Recurrent Neural
Networks, Convolutional Neural Networks).
9. open_file()
Work Detail: Opens file dialog, reads, and displays text content for analysis.
clear_text_widget(widget)
Work Detail: Clears specified text widget to facilitate data management.
clear_result_text()
Work Detail: Clears result display widget to prepare for new analysis.
get_file_tokens()
Work Detail: Extracts and displays tokens (words) from text for analysis.
text_to_speech(text, lang='en')
Work Detail: Converts text to speech and plays audio for auditory feedback.
analyze_sentiment()
Work Detail: Conducts sentiment analysis on displayed text for insights.
get_file_pos_tags()
Work Detail: Identifies and displays part-of-speech tags of text for linguistic analysis.
10. SYSTEM ANALYSIS
Existing System:
Manual Review Analysis:
Traditional methods involve manual analysis of
restaurant reviews, which is time-consuming and
subjective.
Limited Insights:
Human-based analysis may overlook important
trends or sentiments due to limitations in processing
large volumes of data.
Inefficient Decision Making:
Lack of automated sentiment analysis hampers the
restaurant's ability to make informed decisions
promptly.
Proposed System:
Automated Sentiment Analysis:
Introduce automated sentiment analysis tools to extract sentiments from
restaurant reviews efficiently.
Comprehensive Insights:
Utilize Natural Language Processing (NLP) techniques to uncover
nuanced sentiments and trends from a vast array of reviews.
Real-time Feedback:
Implement a system that continuously monitors and analyzes incoming
reviews, providing real-time feedback to address customer concerns
promptly.
Customized Solutions:
Tailor sentiment analysis algorithms to capture specific aspects of the
dining experience, such as food quality, service, ambiance, etc., to provide
more targeted insights for improvement.
11. DATA FLOW
External Entities: External entities
represent sources or destinations of data
outside the system being modeled. They
interact with the system but are not part of
it.
Processes: Processes represent
transformations or
manipulations of data within
the system. They take input
data, perform some processing,
and produce output data.
Data Stores: Data stores represent
repositories where data is stored within
the system. They can be physical
locations like databases or files, or they
can be temporary storage areas like
buffers or queues.
Data Flows: Data flows
represent the movement of data
between external entities,
processes, and data stores. They
indicate the path data takes as it
moves through the system.
12. S Y S T E M I M P L E M E N T I O N
1.GUI Setup: Utilizes Tkinter to create a GUI window with specified dimensions, title, and background color.
2.GUI Elements: Includes labels, buttons, and scrolled text widgets for displaying file content and analysis results.
3.File Handling: Enables opening text files, displaying their content, and clearing the displayed text.
4.Text Analysis Functions: Provides functionality for tokenization, sentiment analysis, and part-of-speech tagging of
text data.
5.Integration with External APIs: Utilizes Google Translate and gTTS for translation and text-to-speech conversion,
respectively.
6.Real-time Analysis: Allows for real-time sentiment analysis and display of sentiment labels based on the analyzed
text.
7.User Interaction: Supports user interaction through buttons for file opening, text analysis triggering, result clearing,
and window closing.
8.Visualization: Presents sentiment analysis results and part-of-speech tags in a visually appealing format using
scrolled text widgets.
9.Text-to-Speech: Converts text to speech and plays the audio using the system's default media player.
10.Error Handling: Incorporates error handling for file selection and ensures graceful termination of the application
window.
