Computerized internet search system and method
The present invention provides a system and method that can search social media and Internet websites, and can analyze and display the results according to a variety of criteria including virality on social media websites. The results are presented in a user friendly format such as a magazine, newsletter, newspaper, or scrapbook.
1. A non-transitory computer-readable storage medium storing instructions executable by a computer system, the non-transitory computer-readable storage medium comprising instructions to:
at a computer system:
receive specified search topic from a user's personal computer, laptop, tablet, smartphone, or other computing device or terminal;
for the specified search topic, search one or more social media networks, news or other websites, blogs or blogging websites and/or e-commerce sites to identify messages posted to the one or more social media or other websites related to the specified topic;
receive a selection of one or more attributes potentially associated with each of the identified messages from the remote computer;
classify each of the identified messages according to the one or more selected attributes;
generate a visual representation indicating a quantity of the identified messages classified according to the one or more selected attributes;
cause the visual representation to be provided to the remote computer;
parsing content of each of the identified messages to determine the one or more attributes from the content of each of the identified messages, wherein the one or more attributes includes a sentiment indicating one of a favorable attitude toward the specified topic, a neutral attitude toward the specified topic, and an unfavorable attitude toward the specified topic, calculating a subjectivity index indicative of what proportion of the identified messages indicate either the favorable attitude toward the specified topic and the unfavorable attitude toward the specified topic as compared to a total of the identified messages that indicate any of the favorable attitude toward the specified topic, the neutral attitude toward the specified topic, and the unfavorable attitude toward the specified topic;
wherein the subjectivity index, I, is calculated according to an equation including:
I =(Total Favorable+Total Negative)/(Total Favorable+Total Negative+Total Neutral),
and wherein: Total Favorable includes a quantity of the identified messages indicating the favorable attitude toward the specified topic;
Total Unfavorable includes a quantity of the identified messages indicating the unfavorable attitude toward the specified topic; and
Total Neutral includes a quantity of the identified messages indicating the neutral attitude toward the specified topic.
2. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more selected attributes includes a time at which each of the identified messages was posted.
3. The non-transitory computer-readable storage medium of claim 2 , wherein the selection of the one or more attributes enables a further selection as to a range of times at which each of the identified messages was posted.
4. The non-transitory computer-readable storage medium of claim 2 , wherein the visual representation includes a graph indicating a number of the identified messages that were posted during each of a number of time periods.
5. The non-transitory computer-readable storage medium of claim 4 , wherein the graph includes a histogram.
6. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more selected attributes includes a geolocation from which each of the identified messages was posted.
7. The non-transitory computer-readable storage medium of claim 6 , wherein the selection of the one or more selected attributes enables a further selection as to a plurality of geolocations to be included.
8. The non-transitory computer-readable storage medium of claim 6 , wherein the visual representation includes a map indicating a quantity of the identified messages that were posted from each of a plurality of geolocations.
9. The non-transitory computer-readable storage medium of claim 6 , wherein the visual representation includes a graph indicating a quantity of the identified messages that were posted from each of a plurality of geolocations.
10. The non-transitory computer-readable storage medium of claim 6 , wherein the geolocation includes one of a local community, a municipality, a state, a province, a region, a nation, and a continent.
11. The non-transitory computer-readable storage medium of claim 1 , wherein the selected attribute includes one of relationships and flows between entities participating in the one or more social media networks.
12. The non-transitory computer-readable storage medium of claim 11 , wherein the visual representation includes a social network visualization representing one of the relationship and the flows between the entities participating in the one or more social media networks.
13. The non-transitory computer-readable storage medium of claim 12 , wherein the visual representation includes a graph indicating a quantity of the identified messages indicating one of the favorable attitude toward the specified topic, the neutral attitude toward the topic and the unfavorable attitude toward the topic.
14. The non-transitory computer-readable storage medium of claim 1 , further comprising including the subjectivity index in the visual representation.
15. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more attributes includes a language in which each of the identified messages was posted.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the visual representation includes a graph indicating the language in which each of the identified messages was posted.
17. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more attributes includes a reference to a future time.
18. The non-transitory computer-readable storage medium of claim 17 , wherein the future time includes at least one of a future time, a future date, or one of a plurality of terms indicating a subsequent time.
19. The non-transitory computer-readable storage medium of claim 18 , wherein the plurality of terms indicating a subsequent time include one or more of soon, later, tonight, tomorrow, next week, next month, and next year.
20. The non-transitory computer-readable storage medium of claim 1 , further comprising semantically analyzing the content of the identified messages based on parsing the content of each of the identified messages.
21. The non-transitory computer-readable storage medium of claim 20 , wherein semantically analyzing the content of the identified messages includes identifying a list of words most frequently included in the identified messages.
22. The non-transitory computer-readable storage medium of claim 20 , wherein the list of words most frequently included in the identified messages includes a predetermined number of entries.
23. The non-transitory computer-readable storage medium of claim 21 , further comprising omitting common connector words from the list of words most frequently included in the identified messages.
24. The non-transitory computer-readable storage medium of claim 23 , wherein the common connector words to omitted from the list of words most frequently included in the identified messages includes one or more of and, but, a, an, or, and the.
25. The non-transitory computer-readable storage medium of claim 20 , further comprising classifying words included in the list of words most frequently included in the identified messages according to parts of speech represented by the words included in the list of words most frequently included in the identified messages.
26. The non-transitory computer-readable storage medium of claim 25 , wherein the parts of speech include two or more of nouns, verbs, adjectives, adverbs, place names, proper names, and words indicative of time.
27. The non-transitory computer-readable storage medium of claim 25 , further comprising determining a quantity of each of the parts of speech represented in the list of words most frequently included in the list of identified messages.
28. The non-transitory computer-readable storage medium of claim 27 , wherein the quantity of each of the parts of speech includes a proportion of the part of speech represented in the list of words most frequently included in the list of identified messages.
29. The non-transitory computer-readable storage medium of claim 27 , further comprising including in the visual representation of the quantity of the words apportioned a pre-determined ratio of the parts of speech represented in the list of words most frequently included in the list of identified messages.
30. The non-transitory computer-readable storage medium of claim 28 , further comprising including in the visual representation of the quantity of the words apportioned according to a pre-determined ratio of the parts of speech represented in the list of words most frequently included in the list of identified messages.
31. The non-transitory computer-readable storage medium of claim 1 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a total.
32. The non-transitory computer-readable storage medium of claim 1 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a fraction.
33. The non-transitory computer-readable storage medium of claim 1 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a percentage.
34. The non-transitory computer-readable storage medium of claim 1 , wherein the visual representation includes one of a table, a map, a histogram, a bar graph, a line graph, and pie chart.
35. The non-transitory computer-readable storage medium of claim 34 , further comprising receiving an election of a desired type of visual representation from the remote computer.
36. The non-transitory computer-readable storage medium of claim 1 , further comprising causing an advertisement to be provided to the remote computer based on the specified topic.
37. The non-transitory computer-readable storage medium of claim 36 , wherein the advertisement is topically related to the specified topic.
38. The non-transitory computer-readable storage medium of claim 36 , wherein the advertisement is selectively associated with the specified topic by an advertiser.
39. The non-transitory computer-readable storage medium of claim 1 , further comprising causing an advertisement to be provided to the remote computer based on information accessible about a user of the remote computer or other computing device.
40. The non-transitory computer-readable storage medium of claim 1 , further comprising eliciting remuneration from a user of the remote computer or other computing device.
41. A computer-implemented method, comprising:
at a server computer system in communication with an Internet enabling communication with server computers hosting one or more social media networks and a remote computer:
receiving for a specified topic from the remote personal computer, laptop, tablet, smartphone, or other computing device;
for the specified topic, searching the one or more social media networks to identify messages posted to the one or more social media web sites related to the specified topic;
receiving a selection of one or more attributes potentially associated with each of the identified messages from the remote computer;
automatically classifying each of the identified messages according to the one or more selected attributes;
generating a visual representation indicating a quantity of the identified messages classified according to the one or more selected attributes;
causing the visual representation to be provided to the remote computer;
parsing content of each of the identified messages to determine the one or more attributes from the content of each of the identified messages wherein the one or more attributes includes a sentiment indicating one of a favorable attitude toward the specified topic, a neutral attitude toward the specified tonic, and an unfavorable attitude toward the specified topic;
calculating a subjectivity index indicative of what proportion of the identified messages indicate either the favorable attitude toward the specified topic and the unfavorable attitude toward the specified tonic as compared to a total of the identified messages that indicate any of the favorable attitude toward the specified topic, the neutral attitude toward the specified tonic, and the unfavorable attitude toward the specified topic; and
wherein the subjectivity index, I, is calculated according to an equation including:
I =(Total Favorable+Total Negative)/(Total Favorable+Total Negative+Total Neutral),
and wherein:
Total Favorable includes a quantity of the identified messages indicating the favorable attitude toward the specified topic;
Total Unfavorable includes a quantity of the identified messages indicating the unfavorable attitude toward the specified topic; and
Total Neutral includes a quantity of the identified messages indicating the neutral attitude toward the specified topic.
42. The computer-implemented method of claim 41 , wherein the one or more selected attributes includes a time at which each of the identified messages was posted.
43. The computer-implemented method of claim 41 , wherein the one or more selected attributes includes a geolocation from which each of the identified messages was posted.
44. The computer-implemented method of claim of claim 43 , wherein the selected attribute includes one of relationships and flows between entities participating in the one or more social media networks.
45. The computer-implemented method of claim 41 , wherein the sentiment is determined to include the neutral attitude toward the specified topic when the sentiment is not determined to indicate either the favorable attitude toward the specified topic or the unfavorable attitude toward the specified topic.
46. The computer-implemented method of claim 41 , further comprising including the subjectivity index in the visual representation.
47. The computer-implemented method of claim 41 , wherein the one or more attributes includes a language in which each of the identified messages was posted.
48. The computer-implemented method of claim 47 , wherein the visual representation includes a graph indicating the language in which each of the identified messages was posted.
49. The computer-implemented method of claim 41 , wherein the one or more attributes includes a reference to a future time.
50. The computer-implemented method of claim 49 , wherein the future time includes at least one of a future time, a future date, or one of a plurality of terms indicating a subsequent time.
51. The computer-implemented method of claim 50 , wherein the plurality of terms indicating a subsequent time include one or more of soon, later, tonight, tomorrow, next week, next month, and next year, or specific dates, points, or ranges of time.
52. The computer-implemented method of claim 41 , further comprising semantically analyzing the content of the identified messages based on parsing the content of each of the identified messages.
53. The computer-implemented method of claim 52 , wherein semantically analyzing the content of the identified messages includes identifying a list of words most frequently included in the identified messages.
54. The computer-implemented method of claim 53 , wherein the list of words most frequently included in the identified messages includes a predetermined number of entries.
55. The computer-implemented method of claim 53 , further comprising omitting common connector words from the list of words most frequently included in the identified messages.
56. The computer-implemented method of claim 55 , wherein the common connector words to omitted from the list of words most frequently included in the identified messages includes one or more of and, but, a, an, or, and the.
57. The computer-implemented method of claim 53 , further comprising classifying words included in the list of words most frequently included in the identified messages according to parts of speech represented by the words included in the list of words most frequently included in the identified messages.
58. The computer-implemented method of claim of claim 57 , wherein the parts of speech include two or more of nouns, verbs, adjectives, adverbs, place names, proper names, and words indicative of time.
59. The computer-implemented method of claim 57 , further comprising determining a quantity of each of the parts of speech represented in the list of words most frequently included in the list of identified messages.
60. The computer-implemented method of claim 59 , wherein the quantity of each of the parts of speech includes a proportion of the part of speech represented in the list of words most frequently included in the list of identified messages.
61. The computer-implemented method of claim 59 , further comprising including in the visual representation the quantity of the parts of speech represented in the list of words most frequently included in the list of identified messages.
62. The computer-implemented method of claim 41 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a total.
63. The computer-implemented method of claim 41 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a fraction.
64. The computer-implemented method of claim 41 , wherein the quantity of the identified messages classified according to the one or more selected attributes is expressed as a percentage.
65. The computer-implemented method of claim 41 , wherein the visual representation includes one of a table, a map, a histogram, a bar graph, a line graph, and pie chart.
66. The computer-implemented method of claim 41 , further comprising causing an advertisement to be provided to the remote computer based on the specified topic.
67. The computer-implemented method of claim 66 , wherein the advertisement is topically related to the specified topic.
68. The computer-implemented method of claim 66 , wherein the advertisement is selectively associated with the specified topic by an advertiser.
69. The computer-implemented method of claim 41 , further comprising causing an advertisement to be provided to the remote computer based on information accessible about a user of the remote computer.
70. The computer-implemented method of claim 41 , further comprising eliciting remuneration from a user of the remote computer.
71. The computer-implemented method of claim 41 , wherein the advertisement is selectively displayed when predetermined levels of virality, popularity, and/or polarity are reached for a specific topic.
72. The computer-implemented method of claim 41 , wherein the user can earn loyalty points or credits by using the system.