Word of Mouth and eWOM Strategies

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Questions and Answers

What defines Word of Mouth (WOM)?

Communication through social media regarding a product.

Oral, person-to-person communication about a brand between a receiver and a communicator perceived as non-commercial. (correct)

Written communication about brands in advertisements.

Oral communication that appears commercial in nature.

What percentage of consumers trust recommendations from family and friends over other forms of advertising?

65%

85%

92% (correct)

72%

Why do consumers engage in Electronic Word of Mouth (eWOM)?

To reduce the cost of advertising.

To increase brand visibility without any personal interaction.

To seek product-related inputs from personal sources. (correct)

To create misleading information about brands.

Which of the following statements about WOM marketing is true?

It involves more than 2.4 billion daily conversations about brands. Signup and view all the answers

Which aspect is examined concerning the impact of eWOM?

The impact of eWOM sentiment on stock prices. Signup and view all the answers

What does the acronym TF-IDF stand for?

Term Frequency - Inverse Document Frequency Signup and view all the answers

In the context of TF-IDF, what does a higher Inverse Document Frequency (IDF) imply about a term?

The term is relatively important and not common across documents. Signup and view all the answers

Which of the following formulas represents Term Frequency (TF)?

TF = count of term in document / total words in document Signup and view all the answers

Why are stop words typically assigned lower weights in the TF-IDF model?

They occur frequently and are less informative. Signup and view all the answers

If a term appears in multiple documents, what effect does this have on its TF-IDF weight?

It decreases the Inverse Document Frequency component for that term. Signup and view all the answers

Which aspect of TF-IDF helps to reduce the importance of common terms?

Inverse document frequency component Signup and view all the answers

Which of the following statements about the counting representation is true?

It shows the frequency of word occurrences across multiple documents. Signup and view all the answers

Which formula correctly combines TF and IDF to calculate TF-IDF?

TF-IDF = tf * idf Signup and view all the answers

What factors influence the perceived credibility of eWOM information?

Quality of the content Signup and view all the answers

What is the relationship between perceived helpfulness of information and consumer behavior?

It increases confidence in using eWOM information. Signup and view all the answers

How does emotional content affect the spread of information online?

Negative emotional content spreads faster than positive content. Signup and view all the answers

What trait of information sources significantly affects perceived credibility?

Attractiveness Signup and view all the answers

Why do vendors with low-quality products manipulate reviews?

To mislead consumers and impact their purchase decisions. Signup and view all the answers

What role does negativity play in online information dissemination?

It enhances engagement and spreads faster. Signup and view all the answers

Which is identified as a reason for the rapid spread of fake news on social media platforms?

Novelty and sensationalism. Signup and view all the answers

What is a consequence of the way emotional content influences social media behavior?

Users are more likely to interact with negative news. Signup and view all the answers

What is the main impact of negative electronic word of mouth (eWOM) on a brand?

It can create a rapid spread of negativity. Signup and view all the answers

Which task involves classifying sentiment in eWOM analysis?

Natural Language Processing classification. Signup and view all the answers

What type of data does eWOM typically consist of?

Unstructured data including text, images, and videos. Signup and view all the answers

What does sentiment analysis aim to identify in eWOM?

The polarity of opinions as positive, negative, or neutral. Signup and view all the answers

Which of the following is NOT a part of Natural Language Processing applications related to opinion mining?

Predicting stock prices. Signup and view all the answers

What is the primary objective of extracting consumer sentiment from eWOM?

To use it in stock price prediction. Signup and view all the answers

How many unique stocks are mentioned in the data collected from Stocktwits.com?

25 unique stocks. Signup and view all the answers

How does monitoring eWOM contribute to brand management?

It allows for quicker responses to negative sentiments. Signup and view all the answers

What is the nature of tweets used for sentiment analysis in stock prediction tasks?

Microblog postings related to stock prices. Signup and view all the answers

Which of the following statements is an example of a negative polarity opinion?

The installation process was confusing and difficult. Signup and view all the answers

What is the primary purpose of a Bidirectional LSTM?

To compute the state of a word using context from both preceding and following words. Signup and view all the answers

What problem does the vanishing gradient issue primarily affect in RNNs?

The ability to maintain context over long sequences. Signup and view all the answers

What feature distinguishes LSTM cells from traditional RNNs?

The use of two distinct memory cell states for information retention. Signup and view all the answers

How does the forward LSTM compute the state for a word in a sentence?

By utilizing the words from the beginning of the sentence up to that word. Signup and view all the answers

What kind of data is primarily utilized for training the BiLSTM model in stock price prediction?

Labeled tweet data regarding sentiment. Signup and view all the answers

What role does pooling play in the context of LSTMs?

It averages or summarizes the outputs from multiple LSTM cells. Signup and view all the answers

Why is the word 'great' treated as unrelated to 'movie' in traditional feedforward models?

Because the model lacks contextual awareness. Signup and view all the answers

What is the key advantage of maintaining a long-term cell state in an LSTM?

It helps preserve gradients, facilitating the learning of long-range dependencies. Signup and view all the answers

Study Notes

Word of Mouth

Consumers actively seek product-related inputs from friends and family.
Millions of conversations about brands happen daily.
Word-of-mouth marketing is considered one of the most effective marketing tactics.
92% of consumers trust recommendations from friends and family more than advertising.

Electronic Word of Mouth (eWOM)

eWOM is defined as online communication about brands, products, or services.
Consumers rely on eWOM for credibility and helpfulness when making purchase decisions.
Reviews, opinions, and ratings contribute to the persuasiveness of eWOM.
Consumers perceive information as credible and helpful based on factors like review quality, source characteristics, and emotional content.

Managing eWOM

Negative eWOM can damage brand perception due to its rapid spread online.
Companies need to monitor eWOM by analyzing reviews and opinions to manage brand reputation.
Social media monitoring tools can identify sentiment trends, categorize mentions by urgency, and track brand and competitor performance.

Sentiment Analysis and Stock Prediction

Consumer sentiment, like positive or negative opinions, can be extracted from unstructured data like tweets.
Sentiment analysis helps predict stock prices by combining Natural Language Processing (NLP) with machine learning.
NLP techniques, including sentiment analysis and polarity detection, are vital for understanding opinions in text data.
Traditional Feedforward Neural Networks struggle with capturing context and semantic relationships between words.
Recurrent Neural Networks (RNNs) address this by passing information between cells through hidden states.
Long Short-Term Memory (LSTM) networks improve on RNNs by introducing long-term and short-term cell states to preserve gradients and capture long-range dependencies in textual data.
Bidirectional LSTMs (BiLSTMs) combine forward and backward LSTM networks to capture context from both directions of a sequence.

Stock Price Prediction Process

Word embeddings are used to represent words in a vector space, capturing semantic relationships.
A BiLSTM model is trained on labeled tweet data to predict sentiment.
The model can then predict sentiment for a large dataset of tweets, allowing for analysis of stock prices.
The average sentiment of tweets for a given stock on a specific day can be used as a predictor for stock price movements.

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Description

Explore the concepts of word of mouth (WOM) and electronic word of mouth (eWOM) in marketing. Learn about the impact of recommendations from friends and family and how online communication influences consumer decisions. Understand the importance of managing eWOM for brand reputation.