Sentiment Analysis : The Key to Better Business Strategies

Sentiment analysis is widely used across various industries and applications to understand and interpret human emotions expressed in text.

Sentiment analysis has become an essential tool across industries, helping organizations understand and act on the emotions and opinions of their audiences. This enables more informed decision-making, improved customer experiences, and better strategic planning.

Here are some of the popular areas where sentiment analysis is commonly applied :

**Product Development, Content Moderation, Customer Feedback, CRM & Support

• Product Reviews to gauge customer satisfaction and areas of improvement.
• Feature Engineering & prioritization to analyze customer feedback on new feature prioritization
• Personalized digital marketing to customize marketing messages based on customer sentiment.
• Customer Churn Prediction to take proactive measures to retail customers.
• User Experience (UX) improvement to help UX designers to understand the user pain points and improve product stability.
• Sentiment analysis uses online communities to detect harmful or toxic content and take appropriate action.
• Sentiment analysis use Spam detection algorithm to filter out spam, fake reviews and inappropriate content.
• Customer Service through sentiment analysis to capture the customer interaction logs like email, conversations, chats, social message, responses to understand the customer sentiment and improve service quality.
• NPS ( Net Promoting Score) to understand the underlying sentiment behind the customer loyalty and advocacy.

**Social Media Monitoring

• Brand promotion and monitoring to understand public perceptions at social platforms on the brand, organization culture, product and services.
• Marketing Lead generation and Campaign effectiveness to measure the promotion impact.
• Sentiment Analysis helps in crisis management by identifying the negative statement and mitigate the damage.

**Market Research & Financial Market Analysis

Sentiment analysis is used to gather consumer insights from different social media platforms to understand the customer opinions and preferences. Also analyze the sentiment towards the competitor's analysis like strength and weakness compare to own.

Traders and financial analysts use sentiment analysis for Stock Market Predictions and create sentiment indices to predict the market trends for informed investment decisions.

**Healthcare

Sentiment analysis applied to have patient experiences, feedback on further healthcare service improvement. Also, sentiment analysis can be used effectively to gather a patient's emotional state based on their interactions in therapy sessions.

**Media & Entertainment

Sentiment analysis is widely used in different online, OTT (Over the Top) and streaming platform to analyze user sentiment towards content to improve recommendation algorithms.

Studios and game developers use sentiment analysis to gauge audience reactions and adjust marketing strategies or future projects.

**Human Resources & People Analytics

Organizations use sentiment analysis through employee feedback like employee surveys, feedback forms, and exit interviews to gauge employee satisfaction , maintaining organizational culture during restructuring and reorganizing.

During recruitment, organizations analyze the sentiment in candidate reviews and social media profiles to understand their attitudes and fit for the company culture.

**Education

Sentiment analysis helps e-learning platforms to assess course effectiveness based on learner feedback and reviews.

**Legal & Compliances

Law firms use sentiment analysis to review contracts and legal documents to identify clauses with potentially negative or risky implications, Also helps in analyzing past case data and public sentiment to assess the potential success or risk of litigation

**Public Relations

PR (Public Relation) agencies use sentiment analysis to monitor the reputation of their clients and manage public relations campaigns effectively and ensure they convey the intended message and tone to the public during Press release.

**Political Analysis

Sentiment analysis is used to track public opinion on political campaign and use the data to predict the election outcomes.

Tools and Framework

• TextBlob, a Python Library
• VADER ( Valance Aware Dictionary and Sentiment Reasoner) , a rule based sentiment analysis tool for social platforms.
• Scikit-learn ,NLTK, TensorFlow, PyTorch, Flair & Spacy , ML,DL,RL for building sentiment analysis tools and use of pretrained models.
• Platform like Hugging Face transformers, IBM Watson Natural Language, Azure Text Analytics, GCP Natural language API, RapidMiner, SentiStrength, Sentiment140, MonkeyLearn etc.

Simple basic approach on "Sentiment Analysis using Logistic Regression"

Steps:

• Import libraries, load, prepare Data, then Data Preprocessing ( clean, make data into CL format)

#Import libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
import nltk
from nltk.corpus import stopwords
import re
#Load and Prepare Data
# Sample data
data = {
    'review': [
        'I loved the movie, it was fantastic!',
        'The movie was terrible and boring.',
        'Great acting and a compelling story!',
        'I did not enjoy the film, it was too slow.',
        'What a masterpiece! Truly moving.',
        'Waste of time, I would not recommend it.'
    ],
    'sentiment': ['positive', 'negative', 'positive', 'negative', 'positive', 'negative']
}

df = pd.DataFrame(data)
#Data Pre-Processing
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

def preprocess_text(text):
    text = re.sub(r'\W', ' ', text)  # Remove non-alphanumeric characters
    text = text.lower()  # Convert to lowercase
    text = re.sub(r'\s+[a-zA-Z]\s+', ' ', text)  # Remove single characters
    text = re.sub(r'\s+', ' ', text)  # Remove multiple spaces
    text = ' '.join(word for word in text.split() if word not in stop_words)  # Remove stopwords
    return text

df['cleaned_review'] = df['review'].apply(preprocess_text)
        

• Convert text to features by using TF-IDF ( Term Frequency-Inverse Document Frequency)
• Train the Model, using logistic regression
• Evaluate the Model by checking the Model's Accuracy and Performance using the test data.
• Make Predictions to see how the model predicts the sentiment of new and unseen reviews.

#Convert Text to Feature
tfidf_vectorizer = TfidfVectorizer(max_features=500)
X = tfidf_vectorizer.fit_transform(df['cleaned_review']).toarray()
y = df['sentiment'].apply(lambda x: 1 if x == 'positive' else 0)  # Convert sentiment to binary

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

#Model Training
model = LogisticRegression()
model.fit(X_train, y_train)

#Model Evaluation
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

#Predeiction
new_reviews = [
    'This movie was amazing, I had a great time watching it!',
    'I hated this film, it was so disappointing.',
]

# Preprocess and transform the new reviews
new_reviews_cleaned = [preprocess_text(review) for review in new_reviews]
X_new = tfidf_vectorizer.transform(new_reviews_cleaned).toarray()

# Predict the sentiment
predictions = model.predict(X_new)
predicted_labels = ['positive' if pred == 1 else 'negative' for pred in predictions]

for review, label in zip(new_reviews, predicted_labels):
    print(f'Review: "{review}" - Sentiment: {label}')        

• Output, in a yaml file

The ideal score of sentiment analysis depend on the context, the specific model being used, and the nature of the dataset.

We must aim for a balance between precision, recall, and F1-score while ensuring that our model's accuracy is competitive within our specific domain. Regularly reviewing confusion matrices and considering human performance benchmarks can also provide valuable context for interpreting these scores.

Some general score for evaluating the performance of Sentiment analysis model.

• Accuracy : 70%-80% range
• Precision : High precision > 80% is good, while false positives are costly.
• Recall : High recall > 80 % is good, to capture all positive instances even at the cost of some false positive
• F1 Score ( harmonic mean of precision and recall) : >0.8 is very good.
• AUC ( Area under curve)-ROC ( Recurrent Operating characteristics) Curve Score : AUC-ROC scores range from 0.5 (random guessing) to 1.0 (perfect classification). An AUC-ROC score above 0.85 is considered good for sentiment analysis, indicating that the model distinguishes well between different sentiment classes.
• MSE ( Mean Squared Error) for Regression Based Sentiment Analysis : In cases where sentiment is treated as a continuous variable (e.g., sentiment score from 1 to 5), Mean Squared Error (MSE) can be used. A lower MSE indicates better performance, but the exact threshold for “good” MSE depends on the specific scale and application.

In conclusion, sentiment analysis is revolutionizing how industries across the board understand and engage with their customers.

By providing deep insights into customer emotions and behaviors, this powerful tool is enabling companies to tailor their strategies, improve products, and enhance overall customer experiences.

From retail to finance, healthcare to entertainment, sentiment analysis is reshaping the way businesses connect with their audiences, driving better outcomes and stronger relationships. As more industries adopt this technology, the ability to anticipate and respond to customer needs will continue to evolve, setting new standards for customer engagement and satisfaction.