In the majority of the countries, radio is considered a popular medium of mass communication with the widest and highest outreach capacity. Radio is accessible by all irrespective of the economic background. The first radio station in India was established in 1927 in Mumbai. Further, it was set up in Calcutta and Delhi in 1936. All India Radio (AIR) services broadcast radio in India. Because of recent developments in science and technology, radio is playing a significant role in spreading awareness, knowledge, and information. Mann Ki Baat is an Indian radio programme, pioneered and hosted by Prime Minister, Shri Narendra Modi in which Shri. Modi addresses Indians. Till date, 74 episodes were aired till February 2021. Because of varied topics and aspects covered, spreading awareness about various important national agenda and actions taken by the government, important national and international happenings, etc. the show has gained popularity in every single segment of society within and outside India.
Because of the significant advancement of digitization, a huge volume of unstructured format data such as news stories, blogs, social networking websites like Twitter,
Textual data analytics, in recent times, has appeared to a favored method to sort out bulky and gigantic textual content. It typically involves extracting hidden themes and explain the documents as per the themes. Earlier research studies illustrate the diverse methods and algorithms to organize documents. Hofmann (1999) proposed an EM algorithm in learning named “Probabilistic Latent Semantic Indexing” (PLSI).
Griffiths and Steyvers (2005) similarly projected an effective approximation algorithm constructed on Gibbs sampling.
For this exploratory research work, the researchers considered the English version of transcripts of the “Mann Ki Baat 2.0” show accessible on https://www.pmindia.gov.in/en/mann-ki-baat/.
Additionally, Python programming and its packages on Orange Canvas 16 to perform the textual data analytics. The synopsis of data collection is described in
|
|
|
|
2020 |
10th |
29 March |
• COVID-19 Pandemic in India |
11th |
26 April |
• Contributions of arrangements by Indians to fight against the COVID-19 pandemic. • Admired farmers for continuing food demands. • The new normal - Practicing of wearing masks |
|
12th |
31 May |
• Nationwide Lockdown • Crisis of Migrants • Relevance of Yoga during Pandemic |
|
13th |
28 June |
• Self-reliance • Migrant workers • Pandemic amongst other things |
|
14th |
26 July |
• Kargil Vijay Diwas • Pandemic • Madhubani masks • Sports and entrepreneurship |
|
15th |
30 August |
• Onam • Made in India (MiI) Applications and toys • Teachers' Day • Dogs working in the security forces |
|
16th |
27 September |
• Storytelling in India • Hitopadesha and Panchatantra • The surgical strikes’ anniversary |
|
17th |
25 October |
• Festivals • 'Vocal for Local' • Khadi • Mallakhamba • Pencil making in Pulwama |
|
18th |
29 November |
• Guru Nanak Ji ‘s 550th Prakash Parv |
|
19th |
27 December |
• Tributes to Sikh Saints • Heritage Week • Indian scriptures • Active alumni network for educational institutions (colleges, universities, schools, and villages) through innovative approaches to engage the alumni |
|
2021 |
20th |
31 January |
• Farm protests • COVID-19 Vaccination drive • Performance of Indian cricket team in Australia |
21st |
28 February |
• Be a warrior, not a worrier • Pariksha Pe Charcha • Nature Conservation • Aatmanirbhar Bharat Abhiyaan |
Further, a general framework for textual data analysis was prepared on Orange Canvas 16.
To explore the recurrence of topics and words and extract the themes using statistical modeling deliberated in 12 episodes of the popular monthly addressing radio program Mann Ki Baat 2.0. (March 2020 to February 2021) by using textual data analysis techniques.
To capture the sentiments of Mann Ki Baat episodes i.e. “positive”, “negative”, and “neutral”.
To apply other textual data analysis techniques such as Topic correlation, Hierarchy clustering, and preparing word clouds from the text.
Using the
|
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
|
|
|
|
||||
Doctor |
35 |
People |
24 |
People |
60 |
Country |
39 |
Family |
31 |
Country |
24 |
Country |
58 |
India |
33 |
People |
30 |
India |
20 |
Corona |
40 |
People |
13 |
Corona |
29 |
Corona |
17 |
Yoga |
30 |
Narasimha Rao Ji |
05 |
Home |
23 |
World |
16 |
Scheme |
26 |
Calamity |
05 |
Quarantine |
17 |
Countryman |
11 |
Friend |
25 |
Corona Pandemic |
04 |
Patients |
16 |
Fight |
10 |
Ayushman Bharat |
22 |
Reliant India |
04 |
Battle |
12 |
Mask |
09 |
Beneficiaries |
12 |
Lockdown |
03 |
Nurse |
09 |
Pandemic |
07 |
Eastern India/Region |
12 |
Defense Sector |
03 |
Social Media |
04 |
Government / Administration |
06 |
Honest Tax Payer |
06 |
Family Member |
03 |
|
|
|
|
||||
Country |
24 |
Toys |
47 |
Story |
44 |
People |
19 |
People |
22 |
Country |
30 |
Farmers |
28 |
Khadi |
18 |
Corona |
15 |
Children |
20 |
Family |
21 |
Unity |
15 |
Countryman |
15 |
Games |
16 |
Life |
21 |
Farmers |
14 |
Mantra |
08 |
Students |
11 |
Vegetables |
20 |
Library |
13 |
Atal Ji |
04 |
Teachers |
11 |
Today |
16 |
Manzoor Bhai |
05 |
Dragon Fruit |
04 |
Indian Breed |
09 |
King |
16 |
Maharishi Valmiki |
05 |
Gandhi Ji |
03 |
Nutrition Month |
04 |
Bhagat Singh |
10 |
Pencil Slat |
04 |
Armed Force | Battle of Kargil | Kargil War |
02 |
Innovation Challenge | Bharat App Innovation |
03 |
Fruit |
09 |
Khadi Mask |
04 |
Farmers of Kutch |
02 |
Cooperation / Freedom Movement |
03 |
Tenali Rama |
07 |
Local Products |
04 |
|
|
|
|
||||
Farmers |
22 |
Country |
21 |
Vegetables Market |
06 |
People |
23 |
Institutions |
12 |
People |
21 |
Rice Mill |
05 |
Friend |
19 |
Alumni |
11 |
Kashmiri Saffron |
21 |
Jhansi |
05 |
Pride |
07 |
Opportunities |
10 |
India |
16 |
Namo App |
04 |
Chia Seed |
04 |
Gurudwara |
12 |
Shri Guru Gobind |
11 |
Road Safety |
04 |
Water Source |
04 |
Guru Sahib / Darbaar Sahib |
09 |
Leopard |
05 |
Toll Plaza |
03 |
Reliant India |
04 |
Sri Aurobindo |
08 |
Social Media |
04 |
Strawberry Festival |
03 |
Pariksha Pe Charcha |
03 |
Guru Nanak Dev |
08 |
Someshwar Beach |
03 |
Incredible India Weekend |
02 |
Drum Stick |
03 |
Bird Watching |
04 |
Srinivasacharya Swami |
02 |
Dr. Rajendra Prasad |
02 |
Bank of River |
02 |
Agricultural Reform |
03 |
Guru Tegh Bahadurji |
02 |
National Yoga Day |
02 |
Corona |
02 |
As shown in
The figure 6 demonstrates the word clouds of Mann Ki Baat. As seen, “Corona”, “Corona Virus”, “Pandemic”, “Quarantine” and Health-care related words are the most highlighted words starting from March – June 2020. It is worthy to note that, COVID -19 / Corona virus pandemic was at pick during this time. Further, “Country”, “Countrymen”, “People”, and “India / Nation” are the words which were spoken repetitively in each episode of MKB 2.0 which shows the ideology where the country, and people are the primary important stakeholders and PM conveys that, he is working for their better and eager to learn about their ideas, issues, and concerns. Shri. Modi makes sure that every citizen of the nation is actively involved in the development path of the nation. Further, “Friend (s) word frequently used during all the episodes indicates, his rapport with the audience. Using this word PM sounds very friendly, free, and open-minded.
Sentiment can be well-defined as an “attitude, thought or judgement prompted by feelings or a specific view or opinion” (Merriam-Webster, 2016).
|
|
|
|
|
1 |
10th Episode |
0.22 |
0.083 |
0.698 |
2 |
11th Episode |
0.149 |
0.065 |
0.787 |
3 |
12th Episode |
0.12 |
0.059 |
0.822 |
4 |
13th Episode |
0.172 |
0.044 |
0.784 |
5 |
14th Episode |
0.159 |
0.042 |
0.799 |
6 |
15th Episode |
0.171 |
0.023 |
0.806 |
7 |
16th Episode |
0.149 |
0.031 |
0.82 |
8 |
17th Episode |
0.187 |
0.014 |
0.799 |
9 |
18th Episode |
0.177 |
0.021 |
0.803 |
10 |
19th Episode |
0.131 |
0.025 |
0.844 |
11 |
20th Episode |
0.149 |
0.025 |
0.826 |
12 |
21st Episode |
0.158 |
0.014 |
0.828 |
Hierarchical clustering obtains the sibling-sibling associations among topics and organizes the topics into a hierarchical tree.
As seen from figure 8, twelve episodes of Mann Ki Baat can be divided into seven different clusters. The vertical axis shows the clusters, whereas the horizontal scale on the dendrogram represents the distance among the clusters. Since the x-axis shows how close the observations were when they were merged into clusters. Looking at the dendrogram of the Mann Ki Baat data, there are clearly two very distinct groups. The right-hand side group contains two more dissimilar clusters. Whereas, most of the observations in the left group are clustering together at about the same height.
Textual Data Analytics plays a powerful technique for qualitative data analysis to extract meaning from the text. This research paper explored and extracted the topics and themes from twelve episodes of Mann Ki Baat 2.0. The work briefly conveys how topic modeling derived topics models from the corpus. Further, the result shows that Shri. Narendra Modi, PM of India addressed the citizens of India covering a wide range of topics, various initiatives, and schemes undertaken by the government. “Corona”, “Corona Virus”, “Pandemic”, “Quarantine” and Health-care related words are the most highlighted words starting from March – June 2020 as seen in the word clouds of Mann Ki Baat. Topics such as Thanking Frontline workers for Covid – 19 Pandemic, Pariksha Pe Charcha, Farmers of India, Innovation Challenge, etc. spread positivity among Indians and gave a ray of hope during a hard time of the pandemic. In almost all the episodes PM mentioned some names of famous personalities and upcoming special days/festivals/events in India which makes a rapport with the common man and builds a positive image of the ruling government and acts as a public relation tool for the PM. Further, there seem to be more neutral Mann Ki Baat episodes compared to positive and negative sentiments. Negative sentiments are hardly observed in all the selected episodes of Mann Ki Baat. This research paper is limited to Mann Ki Baat episodes aired from March 2020 to February 2021. The researchers have avoided discussion on the topic on various social media platforms which can potentially influence the results and sentiment associated with textual information. Nevertheless, these restrictions similarly open up prospects for more research into the left-out knowledge domains. Further, the research can be extended to take related content from Twitter, Facebook, any other social media platforms, and more challenging textual data analysis can be done.