2. The computing system of claim 1 , when the first term or the second term is associated with a positive sentiment or opinion type, the derived sentiment or opinion type is negative; when the first term or the second term is associated with a negative sentiment or opinion type, the derived sentiment or opinion type is positive.
1. A computing system having machine-intelligence in processing natural language contents, comprising at least a processor which is configured to: receive a text content comprising a text units, wherein the text unit comprises a first term and a second term as components in the text unit, each term comprising a word or a phrase in a language; identify, in the text content, the text unit for assigning a derived sentiment or opinion type for the text unit as a whole based on the first term and the second term, wherein the derived sentiment or opinion type of the text unit as a whole is unknown when the text content is received, wherein neither the first term nor the second term includes a grammatically defined negator or negation word of the language; discover a first semantic attribute associated with the first term, wherein the first semantic attribute is not indicated in the text unit or in the text content when received, wherein the first semantic attribute is represented by a first label or a first group membership associated with the first term, and is discovered from a data source external to the text content, wherein the first semantic attribute is not a positive or negative sentiment or opinion type; discover a sentiment or opinion type associated with the second term, wherein the sentiment or opinion type is not indicated in the text unit or in the text content when received, wherein the sentiment or opinion type is discovered from a data source external to the text content; perform one of the following to generate a derived sentiment or opinion type for the text unit as a whole, including a sentiment or opinion type that is different or opposite to the sentiment or opinion type associated with the second term: (a) generating a positive sentiment or opinion type for the text unit as a whole if the first semantic attribute associated with the first term represents a meaning referring to a process or action of increasing, adding, or becoming more, and if the second term is associated with a positive sentiment or opinion type, (b) generating a negative sentiment or opinion type for the text unit as a whole if the first semantic attribute associated with the first term represents a meaning referring to a process or action of increasing, adding, or becoming more, and if the second term is associated with a negative sentiment or opinion type, (c) generating a positive sentiment or opinion type for the text unit as a whole if the first semantic attribute represents a meaning referring to a process or action of decreasing, subtracting, or becoming less, and if the second term is associated with a negative sentiment or opinion type, and (d) generating a negative sentiment or opinion type for the text unit as a whole if the first semantic attribute represents a meaning referring to a process or action of decreasing, subtracting, or becoming less, and if the second term is associated with a positive sentiment or opinion type; and extract, as a data object, the text unit from the text content with the derived sentiment or opinion type attached to it, or displaying, in a user interface, the text unit as a whole with the derived sentiment or opinion type indicated.