In the digital age we live in, social media, online forums, and other digital platforms have become new virtual squares where people share their opinions and feelings. With an enormous amount of data generated every day, it becomes essential to understand and analyze the sentiment expressed in online communications. In this context, sentiment analysis comes into play, a discipline that leverages artificial intelligence to interpret human emotions.
What is Sentiment Analysis?
Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique aimed at identifying, extracting, and evaluating sentiment within a text. Through the analysis of keywords, sentiment analysis seeks to determine whether the text expresses a positive, negative, or neutral emotion.
Use Cases
Sentiment analysis has various practical applications across different sectors.
- Marketing: by analyzing comments from social media or online reviews, companies can assess customer opinions in real-time about their services or products and adjust their strategy quickly and effectively. This technique can uncover customer needs that may become new business opportunities.
- Brand Reputation: Sentiment Analysis helps identify and promptly manage negative comments or discussions on social media. Having an immediate analysis of public opinion is essential to avoid reputation management crises and ensure timely intervention through crisis management strategies.
- Identifying Issues: reading consumer comments allows for the identification of potential problems or weaknesses in products or services offered, enabling the company to act swiftly, especially in the case of new products launched on the market. Companies can also use sentiment analysis to monitor internal company mood or evaluate the effectiveness of an advertising campaign.
- Trends and Activities: Sentiment Analysis allows observing competitor trends and activities. This aspect is crucial for evaluating the performance of one’s company.
How Does It Work?
Sentiment Analysis uses artificial intelligence and machine learning algorithms to analyze texts and identify the emotions associated with them. The analysis process involves several phases:
Data Extraction
Relevant data for a specific company, product, brand, or service can be extracted from:
- Internal Sources: Business information acquired from emails, surveys, customer support, databases, etc.
- External Sources: information acquired from social media, news articles, online reviews, forums, etc. In this case, specific techniques such as “Web Scraping” (data extraction from the web) can be utilized.
Data pre-processing
Before applying sentiment analysis, texts need to be processed to be suitable for a subsequent machine learning model. Common steps include:
- Cleaning: removing special characters, stop-words (words of negligible value like “and,” “the,” “a”…), punctuation, and anything that is irrelevant for the analysis.
- Tokenization: breaking down texts into smaller units (“tokens”). A practical example is transforming a sentence into a list of its words.
- POS (Part-of-speech) Tagging: assigning a grammatical category (such as noun, verb, adjective, and adverb) to each token.
- Lemmatization/Stemming: reducing each token to its base form, focusing mainly on the root of the word (thus, words like “processing,” “process,” and “processed” will be grouped).
- Text Transformation: converting language into a format understandable by computers, assigning quantitative representations to texts. Through different methodologies (Bag-of-words, TF-IDF, one-hot encoding…), words or documents are transformed into lists of numbers (vectors), providing the necessary numerical inputs for future models.
Model Building and Classification
This step involves multiple phases:
- Creating Training, Validation, and Test Sets: pre-processed data is divided into three distinct sets: training, validation, and test sets. The training set is used to train the model, the validation set is used to adjust model parameters, and the test set is used to evaluate the model’s final performance.
- Choosing the Model Architecture: for optimal performance, it is necessary to evaluate and choose the appropriate model based on the problem’s complexity and the available data. Available options include Random Forest, Ensemble methods, Logistic Regression, Neural Network models (such as Transformers), etc. Among the latter, we find BERT (Bidirectional Encoder Representations from Transformers), developed by Google in 2018. Unlike previous methods, BERT is pre-trained on vast amounts of text, learning a language representation that considers context and semantics. BERT’s main benefit in sentiment analysis lies in its ability to understand the context of words within a sentence or an entire text, capturing subtle semantic nuances and accurately understanding the text’s implied emotion. Another advantage is its availability in over 100 languages.
- Model Training: using the training set, the model is trained to classify texts correctly based on their sentiment, minimizing error.
- Performance Evaluation: once trained, the model’s performance is analyzed using the validation set, calculating metrics such as accuracy, precision, recall, or F1-score.
Visualizing Results
Once the analysis results are extracted, they are represented and explained through an intuitive and informative dashboard. The dashboard provides a clear and comprehensible visualization of the collected data using graphs, tables, and other interactive visual elements.
The dashboard is an essential tool for explicitly showing the trends emerging from the analysis.
Challenges and Limitations
Despite significant advancements in sentiment analysis, several challenges and limitations remain. Context and irony can make it challenging to interpret a text’s sentiment correctly. Additionally, sentiment analysis can be influenced by cultural and linguistic variation. For instance, some idiomatic expressions may be ambiguous for an algorithm.
Blue BI and Sentiment Analysis
Blue BI, which has always believed in the value of data, helps companies use Sentiment Analysis to interpret human emotions, enabling strategic decision-making and timely intervention when needed.
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