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Global Sentiment Analytics Market to reach USD XXXX billion by 2027.

Global Sentiment Analytics Market Size study, by Enterprise Size (Small and medium sized enterprise, large enterprise) by End-Users (BFSI, Media and telecom, Government, Healthcare, Manufacturing, Retail, Others) by Deployment (Cloud Based, On premise) and Regional Forecasts 2021-2027

Product Code: ICTICTS-34226734
Publish Date: 17-10-2021
Page: 200

Global Sentiment Analytics Market is valued approximately USD XXXX billion in 2020 and is anticipated to grow with a healthy growth rate of more than XXXX % over the forecast period 2021-2027. Sentiment Analytics is an evolving field that performs artificial intelligence techniques to identify emotions and opinions expressed in a text message. It is most popular for social media platforms. The increasing demand of AI, ML, NLP will help the business man to grab business opportunity. According to Sentiment Analysis of Impact Technology, in the end of year 2020, 42% of the work in companies will be done on machines as compared to 29% in 2018. Along with this, as technological advancement and modernization in software increases, sentiment market will develop vast opportunity for the data science business. However, lack of awareness among consumer is the major factor that is restraining the growth of the market in the forecasted period 2021-2027.

The key regions considered for the global Sentiment Analytics market study includes Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is leading the market of sentiment analytics in the forecasted period. As large number of market players are present in the region. As the region is developed the new innovation in technology is possible. Whereas, Asia-Pacific is also anticipated to exhibit highest CAGR over the forecast period 2021-2027. As there is technological advancement and there is increased adoption of analytics solution by number industries. Which will increase the growth of sentiment analytics in the region over the forecasted period.

Major market player included in this report are:
Brandwatch
Clarabridge, Inc.
IBM Corporation
Lexalytics, Inc.
MeaningCloud LLC
MonkeyLearn Inc.
NetOwl
RapidMiner, Inc.
Repustate Inc.
SAS Institute Inc.

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Enterprise Size:
Small and medium sized enterprise
Large enterprise
By End-Users:
BFSI
Media and telecom
Government
Healthcare
Manufacturing
Retail
Others
By Deployment:
Cloud Based
On premise
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027

Target Audience of the Global Sentiment Analytics Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Sentiment Analytics Market, by Region, 2019-2027 (USD Billion)
1.2.2. Sentiment Analytics Market, by Enterprise Size, 2019-2027 (USD Billion)
1.2.3. Sentiment Analytics Market, by End-Users, 2019-2027 (USD Billion)
1.2.4. Sentiment Analytics Market, by Deployment, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Sentiment Analytics Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Sentiment Analytics Market Dynamics
3.1. Sentiment Analytics Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Increase business opportunities
3.1.2. Market Challenges
3.1.2.1. Lack of awareness among consumer
3.1.3. Market Opportunities
3.1.3.1. Increasing technological advancement
Chapter 4. Global Sentiment Analytics Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 market scenario
Chapter 6. Global Sentiment Analytics Market, by Enterprise Size
6.1. Market Snapshot
6.2. Global Sentiment Analytics Market by Enterprise Size, Performance – Potential Analysis
6.3. Global Sentiment Analytics Market Estimates & Forecasts by Enterprise Size 2018-2027 (USD Billion)
6.4. Sentiment Analytics Market, Sub Segment Analysis
6.4.1. Small and medium sized enterprise
6.4.2. Large enterprise
Chapter 7. Global Sentiment Analytics Market, by End-Users
7.1. Market Snapshot
7.2. Global Sentiment Analytics Market by End-Users, Performance – Potential Analysis
7.3. Global Sentiment Analytics Market Estimates & Forecasts by End-Users 2018-2027 (USD Billion)
7.4. Sentiment Analytics Market, Sub Segment Analysis
7.4.1. BFSI
7.4.2. Media and telecom
7.4.3. Government
7.4.4. Healthcare
7.4.5. Manufacturing
7.4.6. Retail
7.4.7. Others
Chapter 8. Global Sentiment Analytics Market, by Deployment
8.1. Market Snapshot
8.2. Global Sentiment Analytics Market by Deployment, Performance – Potential Analysis
8.3. Global Sentiment Analytics Market Estimates & Forecasts by Deployment 2018-2027 (USD Billion)
8.4. Sentiment Analytics Market, Sub Segment Analysis
8.4.1. Cloud Based
8.4.2. On Premise
Chapter 9. Global Sentiment Analytics Market, Regional Analysis
9.1. Sentiment Analytics Market, Regional Market Snapshot
9.2. North America Sentiment Analytics Market
9.2.1. U.S. Sentiment Analytics Market
9.2.1.1. Enterprise Size breakdown estimates & forecasts, 2018-2027
9.2.1.2. End-Users breakdown estimates & forecasts, 2018-2027
9.2.2. Canada Sentiment Analytics Market
9.3. Europe Sentiment Analytics Market Snapshot
9.3.1. U.K. Sentiment Analytics Market
9.3.2. Germany Sentiment Analytics Market
9.3.3. France Sentiment Analytics Market
9.3.4. Spain Sentiment Analytics Market
9.3.5. Italy Sentiment Analytics Market
9.3.6. Rest of Europe Sentiment Analytics Market
9.4. Asia-Pacific Sentiment Analytics Market Snapshot
9.4.1. China Sentiment Analytics Market
9.4.2. India Sentiment Analytics Market
9.4.3. Japan Sentiment Analytics Market
9.4.4. Australia Sentiment Analytics Market
9.4.5. South Korea Sentiment Analytics Market
9.4.6. Rest of Asia Pacific Sentiment Analytics Market
9.5. Latin America Sentiment Analytics Market Snapshot
9.5.1. Brazil Sentiment Analytics Market
9.5.2. Mexico Sentiment Analytics Market
9.6. Rest of The World Sentiment Analytics Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Brandwatch
10.2.1.1. Key Information
10.2.1.2. Overview
10.2.1.3. Financial (Subject to Data Availability)
10.2.1.4. Product Summary
10.2.1.5. Recent Developments
10.2.2. Clarabridge, Inc.
10.2.3. IBM Corporation
10.2.4. Lexalytics, Inc.
10.2.5. MeaningCloud LLC
10.2.6. MonkeyLearn Inc.
10.2.7. NetOwl
10.2.8. RapidMiner, Inc.
10.2.9. Repustate Inc.
10.2.10. SAS Institute Inc.
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

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Data Collection:
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Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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