Download The Impact of Big Data on Sentiment Analysis

The Impact of Big Data on Sentiment Analysis.
Introduction
Social media is constantly going into and affecting all aspects of our life (Watheq Ghanim
Mutasher, 2022). Facebook,every 60 seconds, 317,000 status updates; 400 new users; 147,000
photos uploaded; and 54,000 links are shared on Facebook. People give feedback through likes,
comments or debates about the post of their interest. Therefore, these likes, comments, posts
cause the assembly of huge information producing Big Data(Kaur et al., 2019). These massive
volumes of data can be used to address business problems you wouldn’t have been able to tackle
before.
The three V's of Big data
Volume- The amount of data matters. With big data, you’ll have to process high volumes of lowdensity, unstructured data.
Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest
velocity of data streams directly into memory versus being written to disk. Some internetenabled smart products operate in real time or near real time and will require real-time evaluation
and action.
Variety refers to the many types of data that are available. Traditional data types were structured
and fit neatly in a relational database. With the rise of big data, data comes in new unstructured
data types. Unstructured and semistructured data types, such as text, audio, and video, require
additional preprocessing to derive meaning and support metadata.
Sentiment analysis, also known as opinion mining, a subfiled of Natural Language Processing
(NLP) that is a text-based data quarrying method ( Gupta, Shashank). Applied in an array of
chars implied ASCII code to procure core aim: To tag, extract, and blind in-depth subjectively
information of a set of the source (Noyes, Dan,2020). It's often used to determine whether the
sentiment expressed in a text is positive, negative, or neutral. The text could be a sentence, a
tweet, a review, a blog post, or any other form of text that expresses an opinion.
Challenges in sentiment analysis at Facebook
Sarcasm and Irony: Sentiment analysis algorithms often struggle to identify and interpret
sarcastic or ironic comments. It is a major challenge to create an algorithm that can understand
the context and the implicit meaning behind a statement.
Dialects and languages: Facebook has a global user base and people use various languages and
dialects to express their sentiments. Interpreting sentiments accurately across all these languages
is a big challenge.
Cultural context: Sentiment is often expressed in ways that are highly dependent on cultural
context. An expression that's positive in one culture could be neutral or negative in another.
Encoding this kind of cultural sensitivity into sentiment analysis algorithms is quite challenging.
Emojis and non-text elements: Users on Facebook frequently use emojis, GIFs, and other nontext elements to express their sentiments. While some of these are straightforward, many others
can have varied meanings based on context. Analyzing these elements accurately is challenging.
Ambiguity and nuance: Human language is full of nuance and ambiguity. A single sentence can
often be interpreted in multiple ways. This makes it hard for algorithms to accurately identify
sentiment.
Noise in the data: User-generated content on platforms like Facebook is often noisy with typos,
non-standard grammar, slang, and other irregularities. This can make it harder for sentiment
analysis algorithms to correctly interpret the text.
Privacy concerns: Analyzing user sentiments involves processing users' personal data. This can
raise privacy concerns, and companies need to ensure they are compliant with relevant
regulations.
Technologies For Sentiment Analysis of Big Data
1. Natural Language Processing (NLP):
NLP is a fundamental technology used in sentiment analysis. It involves the use of
computational linguistics and machine learning algorithms to understand and process human
language. NLP enables Facebook to analyze text data, such as posts, comments, and messages,
to extract sentiment and gain insights into user opinions.
2. Machine Learning Algorithms:
Machine learning plays a critical role in sentiment analysis. Supervised learning models, such as
Support Vector Machines (SVM), Logistic Regression, and Neural Networks, are commonly used
to classify text into positive, negative, or neutral sentiments. These models are trained on large,
labeled datasets to improve accuracy.
3. Deep Learning and Neural Networks:
Deep learning, particularly using neural network architectures like Convolutional Neural
Networks (CNNs) and Recurrent Neural Networks (RNNs), has been increasingly employed for
sentiment analysis. These networks can capture complex patterns and long-term dependencies
in text data, leading to more nuanced sentiment understanding.
Lexicon-Based Approach
These approaches calculate emotional orientation of a document from the semantic orientation
of words or phrases in the document. These dictionary-based approaches consist of dictionary
of number of words annotated with their polarity, strength, and semantic orientation. These
lexical techniques make use of a dictionary with a large number of terms annotated with their
polarity, strength, and semantic orientation
With increase in access to Internet and more people coming online and using e-commerce.
Textual information on internet is increasing every second, and it is a challenge to read and
process this vast data set in efficient manner.
User Engagement Signals:
Beyond textual data, Facebook might leverage user engagement signals, such as likes, dislikes,
reactions, emojis, and shares, to gauge user sentiment towards content and posts. These signals
provide additional context for sentiment analysis.
Deep Semantic Analysis:
Deep semantic analysis techniques go beyond simple sentiment classification. These techniques
aim to understand the broader context and intentions behind user messages, enabling a more
sophisticated sentiment analysis by using deep learning methods and neural networks to
perform sentiment analysis.
Data Pre-processing and Feature Extraction.
Data preprocessing involves cleaning and preparing the raw text data to make it suitable for
analysis. The key steps in data preprocessing for sentiment analysis include:
Text cleaning – Removing irrelevant information.
Tokenization – Breaking down the text into individual words.
Stopwords removal – removing the common and uninformative words. (eg. “the”, “and”, “is”)
Stemming – Reducing the word to root form.
Feature extraction.
Feature extraction involves converting the preprocessed text data into numerical
representations or features that can be used by machine learning models for sentiment analysis.
Bag-of-Words (BoW):
Representing the text data as a sparse vector that counts the occurrences of each word in the
vocabulary. BoW disregards word order but captures word frequency.
Term Frequency-Inverse Document Frequency (TF-IDF):
Weighing the importance of words in a document relative to their frequency in the entire
dataset. Words that are more unique to a specific document receive higher weights.
Word Embeddings:
Using pre-trained word embeddings like Word2Vec, GloVe, or FastText to represent words as
dense vectors in a continuous vector space. Word embeddings capture semantic relationships
between words and improve sentiment analysis accuracy.
How Facebook leverage big data for sentiment analysis?
As one of the largest social media platforms in the world, Facebook leverages big data in various
ways to perform sentiment analysis effectively. The vast amount of data generated on Facebook
provides valuable insights into user sentiments, opinions, and interactions.
Data Collection: Facebook collects a massive volume of user-generated content, including text
posts, comments, likes, reactions, shares, and multimedia content (images, videos). This data
serves as the primary source for sentiment analysis.
Real-Time Data Processing: Big data infrastructure allows for real-time or near real-time data
processing. This capability is crucial for sentiment analysis, as it enables Facebook to respond
quickly to user interactions, monitor emerging trends, and detect potential issues.
Language and Demographic Analysis: Facebook operates in multiple languages and is used by
people worldwide. Big data analytics enable sentiment analysis to be performed across different
languages and demographics, providing a more comprehensive understanding of user
sentiments globally.
Content Moderation: Big data-driven sentiment analysis helps Facebook with content
moderation. Sentiment analysis algorithms can flag potentially harmful content, hate speech, or
inappropriate material, enabling Facebook to take necessary actions to maintain a safe and
positive user experience.
Personalization and Recommendation: Facebook uses sentiment analysis to personalize user
experiences and content recommendations. Understanding user sentiments helps in tailoring
content, ads, and recommendations to better align with individual preferences.
Improving User Experience: Facebook uses sentiment analysis to analyze user feedback,
comments, and interactions to identify pain points, user satisfaction levels, and areas for
improvement in its platform and services.
Data-Driven Decision Making: Sentiment analysis on big data plays a crucial role in Facebook's
data-driven decision-making processes. The insights obtained from sentiment analysis are used
to refine algorithms, develop new features, and make strategic decisions.
Advantages and benefits of using big data in sentiment analysis:
Improved Accuracy and Precision: Big data provides a vast and diverse dataset, allowing
sentiment analysis models to be trained on a more comprehensive range of language patterns
and expressions. This leads to improved accuracy and precision in sentiment classification,
enabling more nuanced understanding of user sentiments.
Real-Time Analysis: Big data technologies facilitate real-time or near real-time sentiment
analysis. With the ability to process massive amounts of data quickly, organizations can monitor
and respond promptly to changing sentiments and emerging trends in the digital space.
Enhanced Personalization: Big data-driven sentiment analysis allows organizations to
understand individual user preferences, interests, and sentiments better. This information can
be used to personalize content, products, and services, leading to higher customer satisfaction
and engagement.
Scalability: Big data technologies are designed to handle large-scale datasets efficiently. As usergenerated content continues to grow exponentially, big data enables sentiment analysis systems
to scale and analyze sentiments across millions or billions of data points.
Rich Source of Insights: Big data contains a wealth of unstructured data, including text, images,
videos, and more. Sentiment analysis on such diverse data sources provides rich and
comprehensive insights into user sentiments across different platforms and channels.
How Big Data improves accuracy and insights.
Larger and Diverse Dataset: Big data encompasses massive volumes of structured and
unstructured data from diverse sources.
Robust Statistical Significance: With big data, the sample size for analysis becomes significantly
larger. This increase in sample size provides more robust statistical significance, reducing the
margin of error in insights and predictions.
Real-Time and Near Real-Time Analysis: Big data technologies enable real-time or near realtime analysis of data streams. Organizations can gain insights and respond to changing trends
and situations promptly, facilitating agile decision-making.
Uncovering Hidden Insights: Big data analytics uses advanced algorithms and machine learning
techniques to identify patterns and correlations that might not be apparent in traditional data
analysis methods. These hidden insights can provide valuable information for business
strategies and decision-making.
Data Integration: Big data technologies enable the integration of various data sources, including
structured and unstructured data, into a unified platform. This integration provides a holistic
view of data, leading to more comprehensive insights and analysis.
How Facebook applies sentiment analysis results?
Content Ranking and News Feed - Positive and engaging content is given higher visibility,
ensuring users see more relevant and enjoyable posts.
Ad Targeting and Relevance: Facebook uses sentiment analysis to gauge users' reactions to ads
and to ensure ad targeting aligns with user sentiments. Positive user responses to specific ads
can lead to better ad relevance and engagement.
Sentiment-Based Content Recommendations: Facebook's recommendation algorithms utilize
sentiment analysis to suggest content that aligns with users' sentiments and preferences. This
includes recommending groups, pages, events, and friends based on shared interests and
positive interactions.
Identifying Brand Advocates and Influencers: Sentiment analysis allows Facebook to identify
influential users and brand advocates who positively impact a brand's image and reputation.
Engaging with these users can amplify positive sentiments and promote brand loyalty.
User Feedback Analysis: Facebook analyzes user feedback and comments to understand user
satisfaction, gather feature requests, and address user concerns. This feedback loop enables
continuous platform improvement and user-driven enhancements.
How does the big data driven sentiment analysis address the potential ethical considerations?
Data Privacy and Consent: Big data-driven sentiment analysis must prioritize user privacy and
data protection. User consent should be obtained before collecting and analyzing their data.
Organizations should be transparent about the types of data being collected, how it will be
used, and provide users with clear options to opt-in or opt-out of data collection and analysis.
Anonymization and Aggregation: To protect individual identities and sentiments, data should
be anonymized and aggregated whenever possible. This means that individual sentiments
should be combined with those of other users to prevent re-identification and maintain user
anonymity.
Data Security: Big data platforms must implement robust security measures to protect user
data from unauthorized access, breaches, or misuse. Data encryption, access controls, and
regular security audits are essential to safeguard sensitive user information.
Responsible Use of Insights: Insights derived from sentiment analysis should be used
responsibly and ethically. Organizations should avoid using sentiment analysis results to
manipulate or exploit users' emotions, and instead, focus on improving user experiences and
understanding user needs.
Real-world examples of big data-driven sentiment analysis at Facebook
Example 1: The Flashback
Celebrating its 10th anniversary, Facebook introduced a unique feature called “Flashback”. This
option allows users to retrieve their social network journey from the day of registration until the
present by presenting a captivating video. The “Flashback” video showcases a collection of
cherished photos and posts that got the most comments and likes over the years with
background music.
Facebook also released other special videos like “Friendversary” to celebrate the anniversary of
two people becoming friends on the platforms.
Besides, users can look forward to a delightful video on their birthdays, making the special days
even more memorable.
Example 2: I Voted
Attempted to increase user engagement and political activity, Facebook conducted a social
experiment during the 2010 midterm elections. They introduced a sticker that allowed users to
declare “I Voted” on their profiles. The sticker had a positive impact on user behavior, as those
who noticed it were most likely to participate in the voting process and express their voting
activities to their friends and families. Among a total of 61 million users, approximately 20% of
users who saw their friends using the sticker clicked on it.
Facebook’s Data Science team analyzed the results and claimed that the combination of the
motivational stickers directly influenced around 60,000 votes to participate in the elections.
Additionally, the concept of social contagion, where the voting behavior of one user influenced
connected users, prompted approximately 280,000 users to vote. Consequently, this led to a
total of 340,000 additional voters in the midterm elections.
Facebook further expanded its involvement in the voting process during the 2016 elections.
They provided users with reminders and directions to their respective polling places, aiming to
encourage even more voter participation.
Example 3: Celebrate Pride
After the Supreme Court’s landmark judgment declaring same-sex marriage as a Constitutional
right, Facebook shows its strong support for marriage equality through a vibrant display called
“Celebrate Pride”. This feature allowed users to transform their profile pictures into rainbowcolored ones, symbolizing solidarity with the LGBTQ+ community. The last time such massive
celebrations were witnessed was in 2013 when 3 million people updated their profile pictures
to display the red equals sign, the logo of the Human Rights Campaign.
The “Celebrate Pride” feature was met with an overwhelming response. Within just a few hours
of its availability, over a million users had already changed their profile pictures to show their
support for the cause.
Example 4: Topic Data
With Topic Data, Facebook empowers marketers with valuable insights into audience responses
regarding to brands, events, activities, and various subjects, while safeguarding users’ personal
information. By utilizing Topic Data, marketers gain a deeper understanding of their target
audience, allowing them to tailor their marketing strategies on Facebook and other platforms.
Previously, such data was available through third-party sources, but it had limitations. The
sample sizes were too small to yield significant results or accurately determining demographics.
However, with Topic Data, user activity is aggregated and stripped of personal information,
resulting in a comprehensive and privacy safe pool of data. Therefore, marketers can now make
informed decisions and effectively engage their audience more than ever before.
By leveraging big data, Facebook can analyze the massive amounts of user-generated content
and interactions to personalized videos, allowing users to relive their social network journey and
celebrate their milestones on the platform. The use of big data technologies enables Facebook
to offer such engaging and meaningful experiences to its users.
Challenges and future directions
Current challenges and limitations
a. Domain Dependency: Sentiment analysis is highly dependent on the domain of the text being
analyzed. Different domains may have varying sentiments for the same words or phrases,
making it challenging to create universally applicable sentiment classifiers.
b. Lack of Resources for Rare-Resource Languages: Most sentiment analysis resources and tools
are available for widely spoken languages like English, but there is a scarcity of such resources
for less common languages, hindering sentiment analysis in these languages.
c. Detecting Sarcasm and Slang: Identifying sarcastic sentences and understanding slang words
is difficult for sentiment classifiers, as these linguistic expressions convey sentiments opposite to
their literal meaning.
d. Handling Heterogeneous Data: Social media data is diverse, including texts, images, videos,
etc. Developing sentiment classifiers that can effectively handle this heterogeneous nature of
data is a challenge.
e. Unreliable and Incomplete Data: Social media posts often contain noise, misspellings,
abbreviations, and incomplete information, leading to less accurate sentiment analysis results.
f. Semantic Relations in Multiple Data Sources: Analyzing an event or topic across multiple
social media platforms requires considering semantic relations between data sources, which
poses challenges for sentiment analysis.
g. Subjectivity Detection: The subjectivity of sentiments may vary based on a user's personality
or political views, making it challenging to accurately interpret a text's sentiment.
h. Spam Detection: Identifying and filtering out spam or fake reviews among social media posts
to ensure accurate sentiment analysis is another significant challenge.
Potential future in big data-driven sentiment analysis
a. Improved Accuracy and Data Quality: Future research should focus on enhancing the
accuracy of sentiment analysis by incorporating methods to handle low-quality and unreliable
data effectively.
b. Multi-Lingual Sentiment Analysis: Developing sentiment classifiers that work well across
multiple languages, including rare-resource languages, will enable sentiment analysis on a
global scale.
c. Real-Time Social Data Analysis: Research efforts should concentrate on real-time analysis of
social media data to enable quick responses and insights for businesses and organizations.
d. Privacy-Preserving Sentiment Analysis: As concerns about privacy and data security grow,
novel approaches to ensure privacy-preserving sentiment analysis without compromising data
utility are essential.
e. Integration with Predictive Analytics: Integrating sentiment analysis with predictive analytics
can lead to more accurate predictions and recommendation systems, benefiting various
industries and applications.
f. Enhanced Handling of Heterogeneous Data: Future research should focus on developing
robust sentiment classifiers capable of effectively handling the diverse types of data found in
social media.
g. Dealing with Domain-Dependent Sentiment Analysis: Addressing the challenge of domain
dependency in sentiment analysis will require innovative techniques to adapt sentiment
classifiers to different domains.
h. Real-Time Social Influence and Information Diffusion: Analyzing real-time social influence
and information diffusion across multiple platforms will provide valuable insights into the
dynamics of social networks.
Conclusion
In this paper, we have explored the impact of big data on sentiment analysis at Facebook. We
examined the significance of big data in sentiment analysis and how it has transformed the way
Facebook comprehends user sentiments and emotions. The integration of big data has enabled
Facebook to analyze vast amounts of user-generated content and extract valuable insights from
the data. Technologies at Facebook: We delved into the technologies employed by Facebook for
sentiment analysis, including Natural Language Processing (NLP) techniques, machine learning
algorithms, and data preprocessing methods. These technologies have played a pivotal role in
making sentiment analysis at Facebook more accurate and efficient. We also brought real-world
examples and explored showcased the practical applications of big data-driven sentiment
analysis at Facebook. From improving ad relevance and crisis response to enhancing user
experience and combating offensive content, big data has had a profound impact on various
aspects of the platform.
Our analysis revealed that the integration of big data has revolutionized sentiment analysis at
Facebook. By harnessing the power of big data, Facebook can gain deeper insights into user
sentiments, preferences, and behavior, leading to more personalized user experiences and
content curation. The utilization of sentiment analysis has not only enhanced user engagement
but also facilitated more effective advertising and crisis management strategies.
Looking ahead, the implications of big data and sentiment analysis in social media are
substantial. As technology continues to advance, sentiment analysis will become more
sophisticated, allowing platforms like Facebook to better understand user sentiments in realtime and on a global scale. However, it is crucial to address the challenges of data privacy, bias,
and ethical considerations to maintain user trust and ensure responsible use of big data in
sentiment analysis.
In conclusion, the combination of big data and sentiment analysis at Facebook has opened in a
new era of understanding user sentiments and emotions. The future holds many opportunities
for sentiment analysis and big data in social media, but it is essential to proceed with
responsibility, and ethical considerations to fully realize their potential while safeguarding user
privacy and user interests. As we move forward, Facebook and other social media platforms will
continue to play a crucial role in shaping the landscape of sentiment analysis and data-driven
insights on a global scale.
Reference
Watheq Ghanim Mutasher, Abbas Fadhil Aljuboori, Real Time Big Data Sentiment Analysis and,
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Information Processing (ICIIP), 454-458.
https://doi.org/10.1109/ICIIP47207.2019.8985873
Gupta, Shashank, "Sentiment Analysis: Concept, Analysis, and Applications,"
Towardsdatascience.com, https://towardsdatascience.com/sentiment-analysis-concept-analysisand-applications-6c94d6f58c17.
Noyes, Dan, "The Top 20 Valuable Facebook Statistics-Updated January 2020," zephoria.com,
https://zephoria.com/top-15-valuable-Facebook-statistics.