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Analysis Competitor Analysis Enhanced User Experience STEPS: 1. DATA COLLECTION Web Scraping: Extract content from the target URL and competitor URLs. Tools like BeautifulSoup or Scrapy in Python can be helpful. 2. TEXT PROCESSING Preprocessing: Clean the content by removing HTML tags, JavaScript, CSS, and other non-textual data. Convert all words to lowercase, and remove punctuation and stopwords (common words like “and the”, etc. that don’t contribute much to the content’s meaning.
BAG OF WORDS REPRESENTATION Vocabulary Building: words Phone Number Data both the target URL and competitor URLs. Vectorization: Represent each URL’s content as a vector based on the vocabulary. 4. VISUALIZATION: WORD CLOUD Use the word frequencies from the BoW representation to generate a word cloud for each URL. Python libraries like wordcloud can be used for this. 5. ANALYSIS AND RECOMMENDATIONS Keyword Comparison: Compare the most frequent words in the target URL with those in the competitor URLs.
Ecommendation: Suggest words that are prominent in competitor URLs but are lacking or underrepresented in Identify gaps or potential opportunities.the target URL. RUN THE BELOW CODE import requests.Tokenization: Convert the cleaned content into the target URL with those in the competitor URLs. individual words or tokens. WHAT IS BAG OF WORDS The Bag of Words (BoW) is a technique commonly used in natural language processing and information retrieval. In this model, a text (such as a sentence or a document) is represented as an unordered set of its words, disregarding grammar and word order but keeping multiplicity. bag of words for ranking FEATURES OF BAG OF WORDS Tokenization: It breaks the text into individual words or tokens.
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